From 5d16966103932c0c1453da80cdfa71e183ea7dea Mon Sep 17 00:00:00 2001
From: Panchadip <165953910+Panchadip-128@users.noreply.github.com>
Date: Sat, 19 Oct 2024 02:16:10 +0530
Subject: [PATCH 1/8] Create Gold Price Predictor using Naive models,
 Regression and Exponential Smoothing

---
 ...ctor using Naive models, Regression and Exponential Smoothing | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 Gold Price Prediction Tool /Gold Price Predictor using Naive models, Regression and Exponential Smoothing

diff --git a/Gold Price Prediction Tool /Gold Price Predictor using Naive models, Regression and Exponential Smoothing b/Gold Price Prediction Tool /Gold Price Predictor using Naive models, Regression and Exponential Smoothing
new file mode 100644
index 0000000000..8b13789179
--- /dev/null
+++ b/Gold Price Prediction Tool /Gold Price Predictor using Naive models, Regression and Exponential Smoothing	
@@ -0,0 +1 @@
+

From 7a0ecf3701ee43c311c4e6e71cabdb4725a9e935 Mon Sep 17 00:00:00 2001
From: Panchadip <165953910+Panchadip-128@users.noreply.github.com>
Date: Sat, 19 Oct 2024 02:16:56 +0530
Subject: [PATCH 2/8] Add files via upload

---
 ...nalysis(Time_Series_Forecasting) (1).ipynb | 3258 +++++++++++++++++
 Gold Price Prediction Tool/README (4).md      |   68 +
 .../gold_monthly_csv (1) (1).csv              |  848 +++++
 3 files changed, 4174 insertions(+)
 create mode 100644 Gold Price Prediction Tool/Gold_Price_Analysis(Time_Series_Forecasting) (1).ipynb
 create mode 100644 Gold Price Prediction Tool/README (4).md
 create mode 100644 Gold Price Prediction Tool/gold_monthly_csv (1) (1).csv

diff --git a/Gold Price Prediction Tool/Gold_Price_Analysis(Time_Series_Forecasting) (1).ipynb b/Gold Price Prediction Tool/Gold_Price_Analysis(Time_Series_Forecasting) (1).ipynb
new file mode 100644
index 0000000000..2a57d52b60
--- /dev/null
+++ b/Gold Price Prediction Tool/Gold_Price_Analysis(Time_Series_Forecasting) (1).ipynb	
@@ -0,0 +1,3258 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "w1sXDi9NKMwX"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import statsmodels.api as sm\n",
+        "import warnings\n",
+        "\n",
+        "\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df= pd.read_csv('/content/gold_monthly_csv (1).csv')\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "42lAAR8YLHmN",
+        "outputId": "dbdf21cc-57c1-43c5-cf99-00f1a536581e"
+      },
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      Date  Price\n",
+              "0  1950-01  34.73\n",
+              "1  1950-02  34.73\n",
+              "2  1950-03  34.73\n",
+              "3  1950-04  34.73\n",
+              "4  1950-05  34.73"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-368e63de-e77e-4cb6-a67d-f959ab33adaf\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1950-01</td>\n",
+              "      <td>34.73</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1950-02</td>\n",
+              "      <td>34.73</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1950-03</td>\n",
+              "      <td>34.73</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1950-04</td>\n",
+              "      <td>34.73</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1950-05</td>\n",
+              "      <td>34.73</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-368e63de-e77e-4cb6-a67d-f959ab33adaf')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-368e63de-e77e-4cb6-a67d-f959ab33adaf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-368e63de-e77e-4cb6-a67d-f959ab33adaf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-7115f182-14e2-4bc8-9c63-f3f98094dd3c\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7115f182-14e2-4bc8-9c63-f3f98094dd3c')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-7115f182-14e2-4bc8-9c63-f3f98094dd3c button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 847,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 847,\n        \"samples\": [\n          \"1988-02\",\n          \"1978-07\",\n          \"1973-05\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 453.66531262670776,\n        \"min\": 34.49,\n        \"max\": 1840.807,\n        \"num_unique_values\": 681,\n        \"samples\": [\n          1283.189,\n          437.452,\n          42.616\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 50
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mPtznwy6WjOq",
+        "outputId": "a4d653e4-e1aa-43b1-8e44-4fc3b117c8a1"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(847, 2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 49
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(f\"Date range of gold prices available from- {df.loc[:,'Date'][0]} to {df.loc[:,'Date'][len(df)-1]}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Xobjeu7DWt8M",
+        "outputId": "e4d1a4ad-75a0-413b-8ef0-97f0c4c8019d"
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Date range of gold prices available from- 1950-01 to 2020-07\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "date =pd.date_range(start='1/1/1950',end='8/1/2020',freq='M')\n",
+        "date"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "acmnjWXFXSw-",
+        "outputId": "0bd8a6ef-e86a-466c-e840-3ef38c0e7d26"
+      },
+      "execution_count": 52,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DatetimeIndex(['1950-01-31', '1950-02-28', '1950-03-31', '1950-04-30',\n",
+              "               '1950-05-31', '1950-06-30', '1950-07-31', '1950-08-31',\n",
+              "               '1950-09-30', '1950-10-31',\n",
+              "               ...\n",
+              "               '2019-10-31', '2019-11-30', '2019-12-31', '2020-01-31',\n",
+              "               '2020-02-29', '2020-03-31', '2020-04-30', '2020-05-31',\n",
+              "               '2020-06-30', '2020-07-31'],\n",
+              "              dtype='datetime64[ns]', length=847, freq='M')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['month']= date\n",
+        "df.drop('Date',axis=1,inplace=True)\n",
+        "df=df.set_index('month')\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 237
+        },
+        "id": "FhoLjjLRhuX1",
+        "outputId": "c6cbc9b8-9b6d-4032-dfa3-57028a394dcf"
+      },
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Price\n",
+              "month            \n",
+              "1950-01-31  34.73\n",
+              "1950-02-28  34.73\n",
+              "1950-03-31  34.73\n",
+              "1950-04-30  34.73\n",
+              "1950-05-31  34.73"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8a9a5e45-fb05-4a6e-8a9b-ba180a72165a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Price</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>month</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1950-01-31</th>\n",
+              "      <td>34.73</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1950-02-28</th>\n",
+              "      <td>34.73</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1950-03-31</th>\n",
+              "      <td>34.73</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1950-04-30</th>\n",
+              "      <td>34.73</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1950-05-31</th>\n",
+              "      <td>34.73</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a9a5e45-fb05-4a6e-8a9b-ba180a72165a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8a9a5e45-fb05-4a6e-8a9b-ba180a72165a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8a9a5e45-fb05-4a6e-8a9b-ba180a72165a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-98790505-6795-4ce3-a8bc-72878a924e72\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-98790505-6795-4ce3-a8bc-72878a924e72')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-98790505-6795-4ce3-a8bc-72878a924e72 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 847,\n  \"fields\": [\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1950-01-31 00:00:00\",\n        \"max\": \"2020-07-31 00:00:00\",\n        \"num_unique_values\": 847,\n        \"samples\": [\n          \"1988-02-29 00:00:00\",\n          \"1978-07-31 00:00:00\",\n          \"1973-05-31 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 453.66531262670776,\n        \"min\": 34.49,\n        \"max\": 1840.807,\n        \"num_unique_values\": 681,\n        \"samples\": [\n          1283.189,\n          437.452,\n          42.616\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 53
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.plot(figsize=(20,8))\n",
+        "plt.title(\"gold prices monthly since 1950 and onwards\")\n",
+        "plt.xlabel(\"months\")\n",
+        "plt.ylabel(\"price\")\n",
+        "plt.grid();"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 304
+        },
+        "id": "2XwaJSP_fz8N",
+        "outputId": "376a2016-3573-438e-9c8e-15237d4026bf"
+      },
+      "execution_count": 54,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x800 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "round(df.describe(),3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "TNbkDCRDiG2R",
+        "outputId": "5b94bb8b-d36a-4cb6-95db-d4eab75edd06"
+      },
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          Price\n",
+              "count   847.000\n",
+              "mean    416.557\n",
+              "std     453.665\n",
+              "min      34.490\n",
+              "25%      35.190\n",
+              "50%     319.622\n",
+              "75%     447.029\n",
+              "max    1840.807"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e101d821-0164-455b-989d-f2cc3dbb9e66\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>847.000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>416.557</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>453.665</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>34.490</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>35.190</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>319.622</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>447.029</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1840.807</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e101d821-0164-455b-989d-f2cc3dbb9e66')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e101d821-0164-455b-989d-f2cc3dbb9e66 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e101d821-0164-455b-989d-f2cc3dbb9e66');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-949bdab3-0d68-4ccb-ab12-5e0714464600\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-949bdab3-0d68-4ccb-ab12-5e0714464600')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-949bdab3-0d68-4ccb-ab12-5e0714464600 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"round(df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 582.5966685511145,\n        \"min\": 34.49,\n        \"max\": 1840.807,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          416.557,\n          319.622,\n          847.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 55
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "_, ax = plt.subplots(figsize=(25, 8))\n",
+        "sns.boxplot(x=df.index.year,y=df.values[:,0],ax=ax)\n",
+        "plt.title('gold price (monthly since 1950 onwards)')\n",
+        "plt.xlabel('year')\n",
+        "plt.ylabel('price')\n",
+        "plt.xticks(rotation=90)\n",
+        "plt.grid();\n",
+        "plt.show"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 426
+        },
+        "id": "zqaRzGuyi-wS",
+        "outputId": "a8d1afac-72a0-45fb-90c6-103a416f627f"
+      },
+      "execution_count": 61,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<function matplotlib.pyplot.show(close=None, block=None)>"
+            ],
+            "text/html": [
+              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
+              "      pre.function-repr-contents {\n",
+              "        overflow-x: auto;\n",
+              "        padding: 8px 12px;\n",
+              "        max-height: 500px;\n",
+              "      }\n",
+              "\n",
+              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
+              "        cursor: pointer;\n",
+              "        max-height: 100px;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <pre style=\"white-space: initial; background:\n",
+              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
+              "         border-bottom: 1px solid var(--colab-border-color);\"><b>matplotlib.pyplot.show</b><br/>def show(*args, **kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py</a>Display all open figures.\n",
+              "\n",
+              "Parameters\n",
+              "----------\n",
+              "block : bool, optional\n",
+              "    Whether to wait for all figures to be closed before returning.\n",
+              "\n",
+              "    If `True` block and run the GUI main loop until all figure windows\n",
+              "    are closed.\n",
+              "\n",
+              "    If `False` ensure that all figure windows are displayed and return\n",
+              "    immediately.  In this case, you are responsible for ensuring\n",
+              "    that the event loop is running to have responsive figures.\n",
+              "\n",
+              "    Defaults to True in non-interactive mode and to False in interactive\n",
+              "    mode (see `.pyplot.isinteractive`).\n",
+              "\n",
+              "See Also\n",
+              "--------\n",
+              "ion : Enable interactive mode, which shows / updates the figure after\n",
+              "      every plotting command, so that calling ``show()`` is not necessary.\n",
+              "ioff : Disable interactive mode.\n",
+              "savefig : Save the figure to an image file instead of showing it on screen.\n",
+              "\n",
+              "Notes\n",
+              "-----\n",
+              "**Saving figures to file and showing a window at the same time**\n",
+              "\n",
+              "If you want an image file as well as a user interface window, use\n",
+              "`.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)\n",
+              "``show()`` the figure is closed and thus unregistered from pyplot. Calling\n",
+              "`.pyplot.savefig` afterwards would save a new and thus empty figure. This\n",
+              "limitation of command order does not apply if the show is non-blocking or\n",
+              "if you keep a reference to the figure and use `.Figure.savefig`.\n",
+              "\n",
+              "**Auto-show in jupyter notebooks**\n",
+              "\n",
+              "The jupyter backends (activated via ``%matplotlib inline``,\n",
+              "``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at\n",
+              "the end of every cell by default. Thus, you usually don&#x27;t have to call it\n",
+              "explicitly there.</pre>\n",
+              "      <script>\n",
+              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
+              "        for (const element of document.querySelectorAll('.filepath')) {\n",
+              "          element.style.display = 'block'\n",
+              "          element.onclick = (event) => {\n",
+              "            event.preventDefault();\n",
+              "            event.stopPropagation();\n",
+              "            google.colab.files.view(element.textContent, 401);\n",
+              "          };\n",
+              "        }\n",
+              "      }\n",
+              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
+              "        element.onclick = (event) => {\n",
+              "          event.preventDefault();\n",
+              "          event.stopPropagation();\n",
+              "          element.classList.toggle('function-repr-contents-collapsed');\n",
+              "        };\n",
+              "      }\n",
+              "      </script>\n",
+              "      </div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 61
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2500x800 with 1 Axes>"
+            ],
+            "image/png": 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vVJsgFNDrGtUGAADTKMoDAAAAANBEVs5RynynpxQXF8uUKVMCrsvLywu4fNGiRWFZPDc5fz0AAKHkdDp9f4szMjKaXJAXCTyqzYEDB2T+/PlN2s6sWbMkPT3dbxmj2gAAwgVFeQAAAABARHO73Y0amry6F3ZxcXHAXtc11fchLnOeWi8tLU0WLVrkt6yh4eTT0tJM7V6jhGL++lAKNLqBSP0jHDR2dAMAQOiZvMeqOapNoPsCkVN/Y/Lz82XEiBH8jQEARBSK8gAAAACAoDDRW9jtdsvESZOlorys0dvJyclp1PtiYuNk+bKlAQvzJ3tnSVVC+0Zn1sdx8ghFfhGJj4+v1es90oaTb23z19c3uoFI4BEOwnV0AwCAv1DdY1ULdF8gIlJRUSGFhYXSt29fiYmJafS+AQAQahTlAQAAAACWMtlb2OPxSEV5mZzsM1Kq4q3rRewo9YgUrRePxxPwA+OqhPZSldTZsjzYR6jnrw/Ue72+nuvV+9CcnoV19WKs74GDcBvdAAAQWKjusQAAsCuK8gAAAAAAS9XVW7ipc4M2ZV7Qqvhko0VyK0cBYL5w+wtUKK9vqN+WDL9bX+/1QD3XRZrfe72uXoyRNsIB7MHkAylAOAn6ua9qxW4Gb3sAAEQIivIAAAAAAMsF6i0cqFdtpM0LGqxRAMJxvnBYp75CeaChflsyxHug37OGhsqn9zrswOQDKUA4Cfa5zxQ7AABYg6I8AAAAAMCIQL1qI21e0GDNGR6O84XbWaBehSLB673enAdSmivQ7xk919Ea8EAKWqtgn/ul3QeLxrZp8X5Wiyo/JvEHt1i2PQAAIgVFeQAAAAAAmiDUc4aj5errVShife91OzyQEo4qKyuloKBAdu3aJd27d5fMzExxOp2h3i2ECA+koLUK1rnvGx0oCAV0RggCALRGFOUBAAAAAIgQdfXwrm/u2HCcBkBExO12NzjiQH091wNp7IgDgXoVigSv97pdNOZnJhK8n9vpNmzYILm5uVJSUiIiIm+88YakpKTItGnTJCsrq0nbCjXmQm+6cDoXAbsK1uhAIvyuAQBaJ4ryAAAAAABEiIZ6eAeaOzYc50x2u90ycdJkqSgva9T7A/VcDyQmNk6WL1va4Af9gXoVikRe73WThcmm/sxErP+5VduwYYPMnTtXhg4dKjNnzpTdu3dLRkaGrFy5UubOnSvZ2dkRVZg3ORe66Qd7auZVVVXJrl275MMPPxRVlX79+onD4WhSVjidi4DdMToQAADWoSgPAAAAAECEqKuHd3091MKxh7fH45GK8jI52WekVMVbM3yto9QjUrRePB5PqyiomS5MBuNnJtL0n1tlZaXk5ubK0KFDZd68eVJZWSn79++X/v37y7x582T27Nny1FNPyfDhwyNmKPtgzgdd88GNAwcOyPz58+t8f6CHAGbNmiXp6el+y+p6cKPmQyL15W3cuLFZWdXnYjDmuZaDW1rNNQQAAABmUZQHAAAAACBC1NXDO1J7qFXFJ0tVUudQ70ZEClWRPNQ/s23btklJSYk88MAD4nA4pLKy0rfO4XDIDTfcINOnT5dt27ZJZmZmyPazKYI1H7Tb7ZYbJk4Sb0V5i/YvUFE9OiZWVixf5jtHTGZViw/CPNcA6lfXaBv1jcjS2qfbAACgGkV5AAAAAABaIJgfUDNvMhqkanR7jpNHLI1r6va+/fZbERHp3bt3wPXVy6vf15p5PJ4WF8nr4q0or/XgRtVpD0hYqeZ2k5OTJTomNihti46JleRk6x5yAawW6vuChqbRCTQiSzhOowMAQChQlAcAAAAAoAWC9QE18yajMRL2bbB1Xk0dO3YUEZF9+/bJgAEDaq3ft2+f3/taM5PFa5fLJbm5C+Tzzz/3e9+XX34pzz33XKO3e8stt0hqaqrfsp49e/pdq1wul6xYvqxRhcmGpgGoiQeWEM7C4b6grml0vF6v5Ofny4gRIwI+iAgAACjKAwAAAADQIsH6gDpc5vBGeDvZO0uqEtpbtj3HySP1Ft6DMY93U4YhHzhwoKSkpMiKFStk3rx5fuuqqqpkxYoVkpqaKgMHDrRsH63WmJ6uVvRyDVS8bmhO+UAaO6d8v379pF+/fn7LSktLxeFwyDPPPCNPPPGExMfH17o2lpaWyi9/+Uu57bbb5JprrmnUKCIul6tR169IndoDCCQc7gvqmkanoqJCCgsLpW/fvhITE2PZvgEAYCcU5QEAAAAAaIFgf0Ad6jm8Ed6qEtobOT+Sk5MlJjZOJAjzeMfExjV6yHCn0ynTpk2TuXPnyuzZs+Xaa6+V8vJy2blzp6xcuVI2bdok2dnZ4nQ6Ld9PKzS1p2tLe7nWLF439yGi5s4HHR8f7xvRwOl0SkZGRq1r444dO0REZMCAAS2adzrQVCINPdzAXNeIRNwXAAAQmSjKAwAAAAAiXqjnuQZCxVHa8BDeVmzP5XLJ8mVLQzZk+OkF15SUFLn99tvlpZdekrvuusv3ns6dO8vtt98uKSkpsnv37rAsuAajp2u493I1NbpBfVOJ1PVwA3NdAwAAwBSK8gAAAAAQQqaGMba7UM9zDZjm67letN7ybdfVc93kkOE1r42NGXb98OHDsnDhQt/rxg67HhKq4bmtIDA1ukGgUQDqGwGg+nsAAAAAEyjKAwAAAECImB7G2M5Mz6sNhFpdPdfLysqkpKTEb9nBgwdlyZIlctNNN0n37t1rbSslJUXi4uJ8r0NduHa73XLDxEnirShv0XYCFfGjY2JlxfJlIb8+trbrS1ZWlmRnZ0tubq7f6AapqamSnZ0tWVlZLc4INAoA81wDAAAgXFCUBwAAAIAQCfUwxnZial7tUGBoftQlUM/13bt319mjfMmSJQGXh9sQ3h6Pp8UF+bp4K8rD4vpo5YNEkfIQUVZWlgwfPlw+/vhjWbt2rYwaNUoyMzNb3EMeaG24LwAAIDJRlAcAAACAEKuKT7ZtQRktFwnFNoQPOwzhnZycLNExsUEpzEfHxAYcmr+m0+ewr+b1esXtdkthYWGdx7Gxc9jb+UGi+jidThk0aJAcPHhQBg0aFLEF+UDnh0j950hTzg+gPtwXAAAQmSjKAwAAAAAQxhiaH01hcgjvugqT1cuKi4ubVZh0uVyyYvmyWkPzB1JUVCQ5OTkyY8YM6dOnT4Pvb+zQ/MXFxTJlypSA6/Ly8gIuD7cRBxA89Z0fIoHPEc4PtFQ4PLAEAACaj6I8AAAAAABhzM49aq0cMpfhd81rqDCZk5NTa1ljC5M1h+av6wGA+jS2Z7Lb7a71AEBZWZnMmjXLb9nBgwdlyZIlctNNN0n37t1rbaesrEx2797te93YBwAQeQKNSCFS/0Mi4TYiBSJPODywBAAAmo+iPAAAAAAACAl67Ee2ugqT9Q2X39zCZLAeAHC73XLDxElN6nm6ZMmSRr0vOiZWVixfRqHLBgI9uNFUgR4qoRCKpqr5wFJdvF6viJy65jJCAwAA4YGiPAAAAAAAYcxR2rJCULC31xKl3QeLxraxZFtR5cck/uAWS7aFxgk0VL5IcIbLD9YDAB6PJyhDQYuIeCvKxePxUHSNcG63WyZOmiwV5WWN/p5AD4kEEhMbJ8uXLeUcAQAAaAUoygMAAAAAIp4dC9fJyckSExsnUrTe8m3HxMaFdO5YX9ssLqKHul0InmA/AGDlAyIiDT8kYuU1JhyuV3bm8XikorxMTvYZKVXx1l1fHKUekaL1PLgBAADQSlCUBwAAAABELDsXrl0ulyxfttSWc8cGatuBAwdk/vz5TdrOrFmzJD093fc61O1C5AnWAyIiga8hwbpmhfp61RpUxSdLVVLnUO8GAAAAIhRFeQAAAABAxLJz4VrE3nPH1mxboOHJ6xuavPp74uPjg76vsK+6riHBekiksdesSLxe2Z3j5JGw3h7CS2lpqRQXF/st83q94na7pbCwkL9pAAC0QhTlAQAAAAARzc6F61AzWVQINDx5MOYmB2oKdA0J5kMiNfMC/Z41hOKdeQn7NoR6FxBBiouLZcqUKQHX5eXlBVy+aNGiZt2fBLqGVL8uLi7mAQAAAMIERXkAAAAAACBut7tJvYXrKiowpDzswORDIvUV73JycgIub27xDs13sneWVCW0t2x7jpNHKPTbWKAHexoaASMtLa1ZWVxDAACIDBTlAQAAAABo5dxut0ycNFkqystavK2aRfyY2DhZvmwphXmgDs3tlQ/DoqLCZnuVlZVSUFAgu3btku7du0tmZqY4nU4Ldw4tFejBnmCN2MM1BACAyEBRHgAAAACACFHXMNf1DVPbmCFqPR6PVJSXSWn3waKxbSzb36jyYyIHt4jH46EoD9SBqRvCW3JyssTExokUrbd82zGxcZKcnNyk79mwYYPk5uZKSUmJiIi88cYbkpKSItOmTZOsrCzL9xENCzTSTCANDSlfU2NHmuEaAgBAZKAoDwAAAACwHbvOr1rfELUigYepbcoQtfEHtzR73wDAjlwulyxftrRRRdeGhievqanTe2zYsEHmzp0rQ4cOlZkzZ8ru3bslIyNDVq5cKXPnzpXs7GwK84Y1Z6SZuoaUr4mRZgAAsBeK8gAAAAAA27Hr/KqBhqgVqX+Y2qYMURuMnvIU+gFEOpfL1ajCaLCGJxc5NWR9bm6uDB06VObNmyeVlZWyf/9+6d+/v8ybN09mz54tTz31lAwfPrxFQ9kHeqjN6/WK2+2WwsLCiH2oLVgYaQYAADQWRXkAAAAAgO3YdX7VQEPUirR8mFrf8MxBKKA3Z3hmAIC/bdu2SUlJiTzwwAPicDiksrLSt87hcMgNN9wg06dPl23btklmZmazc+p7qC0vLy/g8kh4qC3YeAANAAA0hKI8AAAAAMB2mF+1acJpeGYACHeBepOL1D9NSkt7k3/77bciItK7d++A66uXV7+vuQI91NbQdT8SHmoLtpO9s6Qqob1l23OcPCIJ+zZYtj0AABB6FOUBAAAAAEBYDM8MAJGgvt7kIoGnSWlpb/KOHTu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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from statsmodels.graphics.tsaplots import month_plot\n",
+        "\n",
+        "fig,ax = plt.subplots(figsize=(22,8))\n",
+        "month_plot(df,ylabel='gold price', ax=ax)\n",
+        "\n",
+        "plt.title('gold price monthly since 1950 onwards')\n",
+        "plt.xlabel('month')\n",
+        "plt.ylabel('price')\n",
+        "plt.grid();\n",
+        "plt.show"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 448
+        },
+        "id": "898SDbuvkIoi",
+        "outputId": "e077c061-bcbd-493a-8def-050931d35c11"
+      },
+      "execution_count": 64,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<function matplotlib.pyplot.show(close=None, block=None)>"
+            ],
+            "text/html": [
+              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
+              "      pre.function-repr-contents {\n",
+              "        overflow-x: auto;\n",
+              "        padding: 8px 12px;\n",
+              "        max-height: 500px;\n",
+              "      }\n",
+              "\n",
+              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
+              "        cursor: pointer;\n",
+              "        max-height: 100px;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <pre style=\"white-space: initial; background:\n",
+              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
+              "         border-bottom: 1px solid var(--colab-border-color);\"><b>matplotlib.pyplot.show</b><br/>def show(*args, **kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py</a>Display all open figures.\n",
+              "\n",
+              "Parameters\n",
+              "----------\n",
+              "block : bool, optional\n",
+              "    Whether to wait for all figures to be closed before returning.\n",
+              "\n",
+              "    If `True` block and run the GUI main loop until all figure windows\n",
+              "    are closed.\n",
+              "\n",
+              "    If `False` ensure that all figure windows are displayed and return\n",
+              "    immediately.  In this case, you are responsible for ensuring\n",
+              "    that the event loop is running to have responsive figures.\n",
+              "\n",
+              "    Defaults to True in non-interactive mode and to False in interactive\n",
+              "    mode (see `.pyplot.isinteractive`).\n",
+              "\n",
+              "See Also\n",
+              "--------\n",
+              "ion : Enable interactive mode, which shows / updates the figure after\n",
+              "      every plotting command, so that calling ``show()`` is not necessary.\n",
+              "ioff : Disable interactive mode.\n",
+              "savefig : Save the figure to an image file instead of showing it on screen.\n",
+              "\n",
+              "Notes\n",
+              "-----\n",
+              "**Saving figures to file and showing a window at the same time**\n",
+              "\n",
+              "If you want an image file as well as a user interface window, use\n",
+              "`.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)\n",
+              "``show()`` the figure is closed and thus unregistered from pyplot. Calling\n",
+              "`.pyplot.savefig` afterwards would save a new and thus empty figure. This\n",
+              "limitation of command order does not apply if the show is non-blocking or\n",
+              "if you keep a reference to the figure and use `.Figure.savefig`.\n",
+              "\n",
+              "**Auto-show in jupyter notebooks**\n",
+              "\n",
+              "The jupyter backends (activated via ``%matplotlib inline``,\n",
+              "``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at\n",
+              "the end of every cell by default. Thus, you usually don&#x27;t have to call it\n",
+              "explicitly there.</pre>\n",
+              "      <script>\n",
+              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
+              "        for (const element of document.querySelectorAll('.filepath')) {\n",
+              "          element.style.display = 'block'\n",
+              "          element.onclick = (event) => {\n",
+              "            event.preventDefault();\n",
+              "            event.stopPropagation();\n",
+              "            google.colab.files.view(element.textContent, 401);\n",
+              "          };\n",
+              "        }\n",
+              "      }\n",
+              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
+              "        element.onclick = (event) => {\n",
+              "          event.preventDefault();\n",
+              "          event.stopPropagation();\n",
+              "          element.classList.toggle('function-repr-contents-collapsed');\n",
+              "        };\n",
+              "      }\n",
+              "      </script>\n",
+              "      </div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 64
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2200x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "_, ax = plt.subplots(figsize=(22, 8))\n",
+        "sns.boxplot(x=df.index.year,y=df.values[:,0],ax=ax)\n",
+        "plt.grid()\n",
+        "plt.xlabel('year')\n",
+        "plt.ylabel('price')\n",
+        "plt.title('gold price (monthly since 1950 onwards)')\n",
+        "plt.xticks(rotation=90)\n",
+        "plt.show"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 457
+        },
+        "id": "AEQ6h9OUlK8P",
+        "outputId": "87a1b227-c60a-4a02-c258-3479aa8699ce"
+      },
+      "execution_count": 67,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<function matplotlib.pyplot.show(close=None, block=None)>"
+            ],
+            "text/html": [
+              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
+              "      pre.function-repr-contents {\n",
+              "        overflow-x: auto;\n",
+              "        padding: 8px 12px;\n",
+              "        max-height: 500px;\n",
+              "      }\n",
+              "\n",
+              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
+              "        cursor: pointer;\n",
+              "        max-height: 100px;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <pre style=\"white-space: initial; background:\n",
+              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
+              "         border-bottom: 1px solid var(--colab-border-color);\"><b>matplotlib.pyplot.show</b><br/>def show(*args, **kwargs)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py</a>Display all open figures.\n",
+              "\n",
+              "Parameters\n",
+              "----------\n",
+              "block : bool, optional\n",
+              "    Whether to wait for all figures to be closed before returning.\n",
+              "\n",
+              "    If `True` block and run the GUI main loop until all figure windows\n",
+              "    are closed.\n",
+              "\n",
+              "    If `False` ensure that all figure windows are displayed and return\n",
+              "    immediately.  In this case, you are responsible for ensuring\n",
+              "    that the event loop is running to have responsive figures.\n",
+              "\n",
+              "    Defaults to True in non-interactive mode and to False in interactive\n",
+              "    mode (see `.pyplot.isinteractive`).\n",
+              "\n",
+              "See Also\n",
+              "--------\n",
+              "ion : Enable interactive mode, which shows / updates the figure after\n",
+              "      every plotting command, so that calling ``show()`` is not necessary.\n",
+              "ioff : Disable interactive mode.\n",
+              "savefig : Save the figure to an image file instead of showing it on screen.\n",
+              "\n",
+              "Notes\n",
+              "-----\n",
+              "**Saving figures to file and showing a window at the same time**\n",
+              "\n",
+              "If you want an image file as well as a user interface window, use\n",
+              "`.pyplot.savefig` before `.pyplot.show`. At the end of (a blocking)\n",
+              "``show()`` the figure is closed and thus unregistered from pyplot. Calling\n",
+              "`.pyplot.savefig` afterwards would save a new and thus empty figure. This\n",
+              "limitation of command order does not apply if the show is non-blocking or\n",
+              "if you keep a reference to the figure and use `.Figure.savefig`.\n",
+              "\n",
+              "**Auto-show in jupyter notebooks**\n",
+              "\n",
+              "The jupyter backends (activated via ``%matplotlib inline``,\n",
+              "``%matplotlib notebook``, or ``%matplotlib widget``), call ``show()`` at\n",
+              "the end of every cell by default. Thus, you usually don&#x27;t have to call it\n",
+              "explicitly there.</pre>\n",
+              "      <script>\n",
+              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
+              "        for (const element of document.querySelectorAll('.filepath')) {\n",
+              "          element.style.display = 'block'\n",
+              "          element.onclick = (event) => {\n",
+              "            event.preventDefault();\n",
+              "            event.stopPropagation();\n",
+              "            google.colab.files.view(element.textContent, 401);\n",
+              "          };\n",
+              "        }\n",
+              "      }\n",
+              "      for (const element of document.querySelectorAll('.function-repr-contents')) {\n",
+              "        element.onclick = (event) => {\n",
+              "          event.preventDefault();\n",
+              "          event.stopPropagation();\n",
+              "          element.classList.toggle('function-repr-contents-collapsed');\n",
+              "        };\n",
+              "      }\n",
+              "      </script>\n",
+              "      </div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 67
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2200x800 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "_, ax = plt.subplots(figsize=(22, 8))\n",
+        "sns.boxplot(x=df.index.month_name(),y=df.values[:,0],ax=ax)\n",
+        "plt.xlabel(\"month\")\n",
+        "plt.ylabel('price')\n",
+        "plt.title('gold price (monthly since 1950 onwards)')\n",
+        "\n",
+        "plt.show();"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 279
+        },
+        "id": "fUVhbRDslqHG",
+        "outputId": "5679ad69-4c03-4de2-b76c-d175569e83c5"
+      },
+      "execution_count": 71,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2200x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_yearly_sum = df.resample('A').mean()\n",
+        "df_yearly_sum.plot();\n",
+        "plt.title(\"avg gold price yearly since 1950\")\n",
+        "plt.xlabel(\"year\")\n",
+        "plt.ylabel(\"price\")\n",
+        "plt.grid();"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "PNcaMQBimQNP",
+        "outputId": "7a815169-ada0-4de3-f0f9-172897c0c25c"
+      },
+      "execution_count": 73,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.quaterly_sum= df.resample('Q').mean()\n",
+        "df.quaterly_sum.plot();\n",
+        "plt.title(\"avg gold price quaterly since 1950\")\n",
+        "plt.xlabel(\"quater\")\n",
+        "plt.ylabel(\"price\")\n",
+        "plt.grid();"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "5Fc-88WnmtyC",
+        "outputId": "21b6c4c5-7e01-4af4-b87a-bdc597b6b1df"
+      },
+      "execution_count": 74,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_decade_sum = df.resample('10A').mean()\n",
+        "df_decade_sum.plot();\n",
+        "plt.title(\"avg gold price decade since 1950\")\n",
+        "plt.xlabel(\"decade\")\n",
+        "plt.ylabel(\"price\")\n",
+        "plt.grid();"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "negv-QtFnG36",
+        "outputId": "0182a8b3-99bd-4299-d811-9d04498d85bd"
+      },
+      "execution_count": 75,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Calculate the standard deviation of the gold price for each year\n",
+        "df_1['std'] = df.groupby(df.index.year)['Price'].std()\n",
+        "\n",
+        "# Calculate the coefficient of variation\n",
+        "df_1['Cov_pct'] = ((df_1['std'] / df_1[\"Mean\"]) * 100).round(2)\n",
+        "\n",
+        "# Display the first five rows of the DataFrame\n",
+        "df_1.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 237
+        },
+        "id": "0180Yc-DnN7v",
+        "outputId": "108744d9-1833-4591-bbd0-9b1830571a07"
+      },
+      "execution_count": 77,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Mean    Min       std  Cov_pct\n",
+              "month                                     \n",
+              "1950   34.729167  34.72  0.002887     0.01\n",
+              "1951   34.717500  34.66  0.020057     0.06\n",
+              "1952   34.628333  34.49  0.117538     0.34\n",
+              "1953   34.879167  34.76  0.056481     0.16\n",
+              "1954   35.020000  34.86  0.082792     0.24"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d1b07a35-64fe-4570-a857-641d8b12ff56\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Mean</th>\n",
+              "      <th>Min</th>\n",
+              "      <th>std</th>\n",
+              "      <th>Cov_pct</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>month</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1950</th>\n",
+              "      <td>34.729167</td>\n",
+              "      <td>34.72</td>\n",
+              "      <td>0.002887</td>\n",
+              "      <td>0.01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1951</th>\n",
+              "      <td>34.717500</td>\n",
+              "      <td>34.66</td>\n",
+              "      <td>0.020057</td>\n",
+              "      <td>0.06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1952</th>\n",
+              "      <td>34.628333</td>\n",
+              "      <td>34.49</td>\n",
+              "      <td>0.117538</td>\n",
+              "      <td>0.34</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1953</th>\n",
+              "      <td>34.879167</td>\n",
+              "      <td>34.76</td>\n",
+              "      <td>0.056481</td>\n",
+              "      <td>0.16</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1954</th>\n",
+              "      <td>35.020000</td>\n",
+              "      <td>34.86</td>\n",
+              "      <td>0.082792</td>\n",
+              "      <td>0.24</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1b07a35-64fe-4570-a857-641d8b12ff56')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d1b07a35-64fe-4570-a857-641d8b12ff56 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d1b07a35-64fe-4570-a857-641d8b12ff56');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-02980ac9-641d-4f5b-bfae-e799455c5ac5\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-02980ac9-641d-4f5b-bfae-e799455c5ac5')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-02980ac9-641d-4f5b-bfae-e799455c5ac5 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_1",
+              "summary": "{\n  \"name\": \"df_1\",\n  \"rows\": 71,\n  \"fields\": [\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 71,\n        \"samples\": [\n          1972,\n          1950,\n          1999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 463.6715316539167,\n        \"min\": 34.62833333333334,\n        \"max\": 1674.830857142857,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          97.1245,\n          34.729166666666664,\n          279.284\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 422.91860711606904,\n        \"min\": 34.49,\n        \"max\": 1585.114,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          129.027,\n          34.72,\n          256.198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34.20059162453,\n        \"min\": 0.0028867513459476476,\n        \"max\": 149.66151710493756,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          8.251596611345203,\n          0.0028867513459476476,\n          16.614292211736554\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cov_pct\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.5192821406057835,\n        \"min\": 0.01,\n        \"max\": 24.69,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          7.1,\n          3.86,\n          0.01\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 77
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig,ax=plt.subplots(figsize=(15,10))\n",
+        "df_1['Cov_pct'].plot();\n",
+        "plt.title(\"gold price (yearly since 1950 onwards)\")\n",
+        "plt.xlabel(\"year\")\n",
+        "plt.ylabel(\"cv in %\")\n",
+        "plt.grid();\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 483
+        },
+        "id": "ThqbSJhZqWKk",
+        "outputId": "ac82bd6f-be32-4e54-9120-081035588e16"
+      },
+      "execution_count": 79,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x1000 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train =df[df.index.year <= 2015]\n",
+        "test = df[df.index.year > 2015]\n"
+      ],
+      "metadata": {
+        "id": "m5S7Xgazqzmc"
+      },
+      "execution_count": 80,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(train.shape)\n",
+        "print(test.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Q3wFp6DRsnkE",
+        "outputId": "dc46ff35-4009-4e02-8f38-71fe5d55f673"
+      },
+      "execution_count": 83,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(792, 1)\n",
+            "(55, 1)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train[\"Price\"].plot(figsize=(13,5),fontsize=15)\n",
+        "test[\"Price\"].plot(figsize=(13,5),fontsize=15)\n",
+        "plt.grid();\n",
+        "plt.legend(['Training Data','Test Data'])\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "id": "pQdxyZKzsyPz",
+        "outputId": "8b4db200-4131-4b7f-f7da-d425b5463d7c"
+      },
+      "execution_count": 85,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1300x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_time=[i+1 for i in range(len(train))]\n",
+        "test_time=[i+len(train)+1 for i in range(len(test))]\n",
+        "len(train_time),len(test_time)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-_Q_Z5_j5DQ_",
+        "outputId": "44158641-4c8e-44f7-e739-3769f4b2653d"
+      },
+      "execution_count": 86,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(792, 55)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 86
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "LR_train = train.copy()\n",
+        "LR_test= test.copy()"
+      ],
+      "metadata": {
+        "id": "vekICndT5oQg"
+      },
+      "execution_count": 87,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "LR_train['time']=train_time\n",
+        "LR_test['time']=test_time"
+      ],
+      "metadata": {
+        "id": "reRsEOfd5u7c"
+      },
+      "execution_count": 89,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lr = LinearRegression()\n",
+        "lr.fit(LR_train[['time']],LR_train['Price'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "NXPUcl8K56XN",
+        "outputId": "594c4dc8-c6c6-4212-febe-d142c3c18848"
+      },
+      "execution_count": 91,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 91
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_predictions_model1 = lr.predict(LR_test[[\"time\"]])\n",
+        "LR_test['forecast']=test_predictions_model1\n",
+        "\n",
+        "plt.figure(figsize=(14,5))\n",
+        "plt.plot(train['Price'],label='train')\n",
+        "plt.plot();\n",
+        "plt.plot(test['Price'],label='test')\n",
+        "plt.plot(LR_test['forecast'],label='reg on time_test data')\n",
+        "plt.legend(loc='upper left')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "id": "y-dbavwt6Jcx",
+        "outputId": "cf513832-a1c2-4e6b-eeae-86303b1d0f01"
+      },
+      "execution_count": 94,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def mape(actual,pred):\n",
+        "  return round((np.mean(np.abs((actual-pred)/actual))*100),2)"
+      ],
+      "metadata": {
+        "id": "4oOVOjs97E6o"
+      },
+      "execution_count": 97,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mape_model1_test=mape(test[\"Price\"].values,test_predictions_model1);\n",
+        "print(\"MAPE is %3.3f\"%(mape_model1_test),\"%\");"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7qLtJ4Fv7E1V",
+        "outputId": "f7992509-f414-4775-9e52-b37e25ce44d2"
+      },
+      "execution_count": 103,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "MAPE is 29.760 %\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results=pd.DataFrame({'Test Mape(%): ': [mape_model1_test]},index=[\"RegressionOnTime\"])\n",
+        "results"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 89
+        },
+        "id": "71LPEulV7Ewj",
+        "outputId": "505a601a-43cd-492b-9fae-b1dd72bf0375"
+      },
+      "execution_count": 107,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  Test Mape(%): \n",
+              "RegressionOnTime           29.76"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b2f2d6e7-5117-411d-ad7f-7fd3b1f3f8e3\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Test Mape(%):</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>RegressionOnTime</th>\n",
+              "      <td>29.76</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2f2d6e7-5117-411d-ad7f-7fd3b1f3f8e3')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b2f2d6e7-5117-411d-ad7f-7fd3b1f3f8e3 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b2f2d6e7-5117-411d-ad7f-7fd3b1f3f8e3');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_3bc8f07b-f397-411e-a35a-e7c29cca061f\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_3bc8f07b-f397-411e-a35a-e7c29cca061f button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('results');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "results",
+              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Test Mape(%): \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 29.76,\n        \"max\": 29.76,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          29.76\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 107
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Naive_train=train.copy()\n",
+        "Naive_test=test.copy()"
+      ],
+      "metadata": {
+        "id": "HbSHyPdU7CQI"
+      },
+      "execution_count": 108,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Naive_test['naive']=np.asarray(train['Price'])[len(np.asarray(train['Price']))-1]\n",
+        "Naive_test.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 237
+        },
+        "id": "udrBLZx785wl",
+        "outputId": "c9df08db-8bc6-4eb2-876b-833d6d5249c3"
+      },
+      "execution_count": 111,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               Price     naive\n",
+              "month                         \n",
+              "2016-01-31  1095.655  1068.317\n",
+              "2016-02-29  1194.893  1068.317\n",
+              "2016-03-31  1246.312  1068.317\n",
+              "2016-04-30  1241.452  1068.317\n",
+              "2016-05-31  1259.755  1068.317"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3177dfb0-fd9c-43db-a12d-2c9e9d0a745a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Price</th>\n",
+              "      <th>naive</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>month</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2016-01-31</th>\n",
+              "      <td>1095.655</td>\n",
+              "      <td>1068.317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-02-29</th>\n",
+              "      <td>1194.893</td>\n",
+              "      <td>1068.317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-03-31</th>\n",
+              "      <td>1246.312</td>\n",
+              "      <td>1068.317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-04-30</th>\n",
+              "      <td>1241.452</td>\n",
+              "      <td>1068.317</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2016-05-31</th>\n",
+              "      <td>1259.755</td>\n",
+              "      <td>1068.317</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3177dfb0-fd9c-43db-a12d-2c9e9d0a745a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3177dfb0-fd9c-43db-a12d-2c9e9d0a745a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3177dfb0-fd9c-43db-a12d-2c9e9d0a745a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-9c7b7193-2a62-4558-a748-873fea218c50\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9c7b7193-2a62-4558-a748-873fea218c50')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-9c7b7193-2a62-4558-a748-873fea218c50 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "Naive_test",
+              "summary": "{\n  \"name\": \"Naive_test\",\n  \"rows\": 55,\n  \"fields\": [\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2016-01-31 00:00:00\",\n        \"max\": \"2020-07-31 00:00:00\",\n        \"num_unique_values\": 55,\n        \"samples\": [\n          \"2018-08-31 00:00:00\",\n          \"2016-06-30 00:00:00\",\n          \"2018-09-30 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.2913533960264,\n        \"min\": 1095.655,\n        \"max\": 1840.807,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          1201.859,\n          1273.58,\n          1199.198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"naive\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.589386593120998e-13,\n        \"min\": 1068.317,\n        \"max\": 1068.317,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1068.317\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 111
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(12,8))\n",
+        "plt.plot(Naive_train['Price'],label='Train')\n",
+        "plt.plot(test['Price'],label='Test')\n",
+        "plt.plot(Naive_test['naive'],label='Naive Forecast on Test Data')\n",
+        "plt.legend(loc='best')\n",
+        "plt.title(\"Naive Forecast\")\n",
+        "plt.grid();\n",
+        "plt.show();"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 476
+        },
+        "id": "gmzGUWQT9X9f",
+        "outputId": "aee6629d-aa21-422e-a530-2d29f9fb111a"
+      },
+      "execution_count": 112,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mape_model2_test=mape(test[\"Price\"].values,Naive_test['naive'].values);\n",
+        "print(\"MAPE is %3.3f\"%(mape_model2_test),\"%\");"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WozZUY8N9MqR",
+        "outputId": "0b8b6125-adf8-4f50-e044-1f5072acb07f"
+      },
+      "execution_count": 113,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "MAPE is 19.380 %\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "resultsDf_2 = pd.DataFrame({'Test MAPE(%): ': [mape_model2_test]},index=[\"NaiveModel\"])\n",
+        "results = pd.concat([results,resultsDf_2]final_model)\n",
+        "results"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 125
+        },
+        "id": "0ZnuGpSI-XPn",
+        "outputId": "1201632a-da6d-4211-e814-78de572e0656"
+      },
+      "execution_count": 117,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  Test Mape(%):   Test MAPE(%): \n",
+              "RegressionOnTime           29.76             NaN\n",
+              "NaiveModel                   NaN           19.38"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ca6b2d87-1271-4df1-a4ba-a8ead86a9b08\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Test Mape(%):</th>\n",
+              "      <th>Test MAPE(%):</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>RegressionOnTime</th>\n",
+              "      <td>29.76</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>NaiveModel</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>19.38</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca6b2d87-1271-4df1-a4ba-a8ead86a9b08')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ca6b2d87-1271-4df1-a4ba-a8ead86a9b08 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ca6b2d87-1271-4df1-a4ba-a8ead86a9b08');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-f4c2fafb-e0fa-443c-849c-674f0b7e115c\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f4c2fafb-e0fa-443c-849c-674f0b7e115c')\"\n",
+              "            title=\"Suggest charts\"\n",
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+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-top-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "    60% {\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f4c2fafb-e0fa-443c-849c-674f0b7e115c button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_f9da2b30-e4d1-4a5e-b7da-02cf0fb91d54\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_f9da2b30-e4d1-4a5e-b7da-02cf0fb91d54 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('results');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "results",
+              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Test Mape(%): \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 29.76,\n        \"max\": 29.76,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          29.76\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Test MAPE(%): \",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 19.38,\n        \"max\": 19.38,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          19.38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 117
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "final_model = ExponentialSmoothing(df,\n",
+        "                                   trend='additive',\n",
+        "                                   seasonal='additive').fit(smoothing_level=0.4,\n",
+        "                                                            smoothing_trend=0.3,\n",
+        "                                                            smoothing_seasonal=0.6)"
+      ],
+      "metadata": {
+        "id": "74DqrVdP-XE7"
+      },
+      "execution_count": 118,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Mape_final_model = mape(df['Price'].values, final_model.fittedvalues)\n",
+        "print(\"MAPE:\", Mape_final_model)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qLySaCXM_mXv",
+        "outputId": "c994b971-ff69-4eb7-a99f-be9184a6bf88"
+      },
+      "execution_count": 122,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "MAPE: 17.24\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "predictions = final_model.forecast(steps=len(test))"
+      ],
+      "metadata": {
+        "id": "EjWpn33UARkN"
+      },
+      "execution_count": 123,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Import the necessary libraries.\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Extract the prediction from the final model.\n",
+        "prediction = final_model.forecast(steps=len(test))\n",
+        "\n",
+        "# Create the DataFrame with the desired columns.\n",
+        "pred_df = pd.DataFrame({'lower_CI': prediction - 1.96*np.std(final_model.resid, ddof=1),\n",
+        "                       'prediction': prediction,\n",
+        "                       'upper_CI': prediction + 1.96*np.std(final_model.resid, ddof=1)})\n",
+        "\n",
+        "# Print the DataFrame.\n",
+        "print(pred_df)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "786ZUteABN5d",
+        "outputId": "ac1c1d94-7ea4-47a4-f9c4-96805660c74a"
+      },
+      "execution_count": 125,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "               lower_CI   prediction     upper_CI\n",
+            "2020-08-31  1684.720065  1792.871037  1901.022009\n",
+            "2020-09-30  1615.306077  1723.457050  1831.608022\n",
+            "2020-10-31  1538.567922  1646.718895  1754.869867\n",
+            "2020-11-30  1476.758600  1584.909572  1693.060545\n",
+            "2020-12-31  1459.327290  1567.478262  1675.629235\n",
+            "2021-01-31  1514.417601  1622.568574  1730.719546\n",
+            "2021-02-28  1545.352396  1653.503369  1761.654341\n",
+            "2021-03-31  1556.764378  1664.915350  1773.066323\n",
+            "2021-04-30  1648.309829  1756.460802  1864.611774\n",
+            "2021-05-31  1694.225915  1802.376887  1910.527859\n",
+            "2021-06-30  1743.402088  1851.553061  1959.704033\n",
+            "2021-07-31  1796.108352  1904.259324  2012.410297\n",
+            "2021-08-31  1785.037437  1893.188409  2001.339381\n",
+            "2021-09-30  1715.623449  1823.774422  1931.925394\n",
+            "2021-10-31  1638.885294  1747.036267  1855.187239\n",
+            "2021-11-30  1577.075972  1685.226944  1793.377917\n",
+            "2021-12-31  1559.644662  1667.795634  1775.946607\n",
+            "2022-01-31  1614.734973  1722.885946  1831.036918\n",
+            "2022-02-28  1645.669768  1753.820741  1861.971713\n",
+            "2022-03-31  1657.081750  1765.232722  1873.383695\n",
+            "2022-04-30  1748.627201  1856.778174  1964.929146\n",
+            "2022-05-31  1794.543287  1902.694259  2010.845231\n",
+            "2022-06-30  1843.719460  1951.870433  2060.021405\n",
+            "2022-07-31  1896.425724  2004.576696  2112.727669\n",
+            "2022-08-31  1885.354809  1993.505781  2101.656753\n",
+            "2022-09-30  1815.940821  1924.091794  2032.242766\n",
+            "2022-10-31  1739.202666  1847.353639  1955.504611\n",
+            "2022-11-30  1677.393344  1785.544316  1893.695289\n",
+            "2022-12-31  1659.962034  1768.113006  1876.263979\n",
+            "2023-01-31  1715.052345  1823.203318  1931.354290\n",
+            "2023-02-28  1745.987140  1854.138113  1962.289085\n",
+            "2023-03-31  1757.399122  1865.550094  1973.701067\n",
+            "2023-04-30  1848.944573  1957.095545  2065.246518\n",
+            "2023-05-31  1894.860659  2003.011631  2111.162603\n",
+            "2023-06-30  1944.036832  2052.187805  2160.338777\n",
+            "2023-07-31  1996.743096  2104.894068  2213.045041\n",
+            "2023-08-31  1985.672180  2093.823153  2201.974125\n",
+            "2023-09-30  1916.258193  2024.409165  2132.560138\n",
+            "2023-10-31  1839.520038  1947.671011  2055.821983\n",
+            "2023-11-30  1777.710716  1885.861688  1994.012661\n",
+            "2023-12-31  1760.279406  1868.430378  1976.581350\n",
+            "2024-01-31  1815.369717  1923.520690  2031.671662\n",
+            "2024-02-29  1846.304512  1954.455485  2062.606457\n",
+            "2024-03-31  1857.716494  1965.867466  2074.018439\n",
+            "2024-04-30  1949.261945  2057.412917  2165.563890\n",
+            "2024-05-31  1995.178030  2103.329003  2211.479975\n",
+            "2024-06-30  2044.354204  2152.505177  2260.656149\n",
+            "2024-07-31  2097.060468  2205.211440  2313.362413\n",
+            "2024-08-31  2085.989552  2194.140525  2302.291497\n",
+            "2024-09-30  2016.575565  2124.726537  2232.877510\n",
+            "2024-10-31  1939.837410  2047.988383  2156.139355\n",
+            "2024-11-30  1878.028088  1986.179060  2094.330033\n",
+            "2024-12-31  1860.596778  1968.747750  2076.898722\n",
+            "2025-01-31  1915.687089  2023.838062  2131.989034\n",
+            "2025-02-28  1946.621884  2054.772857  2162.923829\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**FINAL GRAPH OF GOLD PRICE CHANGE WITH TIME FORECAST**"
+      ],
+      "metadata": {
+        "id": "jYwSBdIFC_BH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "axis= df.plot(label='Actual',figsize=(16,9))\n",
+        "pred_df['prediction'].plot(ax=axis,label='Forecast', alpha=0.5)\n",
+        "axis.fill_between(pred_df.index,\n",
+        "                 pred_df['lower_CI'],\n",
+        "                 pred_df['upper_CI'],\n",
+        "                 color='k',\n",
+        "                 alpha=0.1)\n",
+        "\n",
+        "axis.set_xlabel('year-month')\n",
+        "axis.set_ylabel('Price')\n",
+        "axis.legend(loc='best')\n",
+        "plt.grid();\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 793
+        },
+        "id": "xz5tMmtmBTCV",
+        "outputId": "7255ef98-8319-406a-8eec-c3cc5a73fffa"
+      },
+      "execution_count": 134,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x900 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Gold Price Prediction Tool/README (4).md b/Gold Price Prediction Tool/README (4).md
new file mode 100644
index 0000000000..22624ad790
--- /dev/null
+++ b/Gold Price Prediction Tool/README (4).md	
@@ -0,0 +1,68 @@
+# Gold-Price-Analysis-with-Time--1950-to-2020-Time-Series-Forecasting-
+Applied statistical and machine learning techniques including Linear Regression, Naive Model, and Exponential Smoothing. Visualized historical trends and seasonal patterns to generate accurate future predictions. Achieved robust forecasting performance with a comprehensive evaluation of model accuracy.
+
+
+OVERVIEW:
+-----------
+This project involves a comprehensive analysis and forecasting of gold prices from January 1950 to August 2020. Using various statistical and machine learning techniques, we aim to understand historical trends and make future predictions.
+
+Dataset
+The dataset contains monthly gold prices spanning from January 1950 to August 2020. The data was processed and analyzed using Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Statsmodels.
+
+METHODOLOGY:
+--------------
+Data Preprocessing:
+
+Loaded the dataset and inspected its structure.
+Converted the 'Date' column to datetime format and set it as the index.
+Conducted exploratory data analysis (EDA) to visualize trends and patterns.
+Exploratory Data Analysis (EDA):
+
+Visualized gold prices over time using line plots.
+Created box plots to analyze price distribution across years and months.
+Generated descriptive statistics to summarize the data.
+Resampled the data to visualize yearly, quarterly, and decadal trends.
+Time Series Decomposition:
+
+Decomposed the time series data to identify trend, seasonal, and residual components.
+Modeling and Forecasting:
+
+Linear Regression: Built a linear regression model to forecast future prices.
+Naive Model: Used the last observed value as the forecast for future periods.
+Exponential Smoothing: Applied the Holt-Winters method to account for trends and seasonality in the data.
+Evaluated models using Mean Absolute Percentage Error (MAPE) to compare accuracy.
+
+KEY RESULTS:
+---------------
+Visualizations:
+
+Line plots and box plots provided insights into price trends and distributions over the years.
+Resampling revealed the long-term trends and seasonal patterns in gold prices.
+Forecasting Models:
+
+Linear Regression: Achieved a MAPE of 29.760%.
+Naive Model: Achieved a MAPE of 19.380%.
+Exponential Smoothing: Achieved a MAPE of 17.241%.
+
+
+FINAL FORECAST:
+-----------------
+
+The final forecast model using Exponential Smoothing provided a robust prediction of future gold prices, considering both trend and seasonality.
+
+
+CONCLUSION:
+-----------------
+This project demonstrates the use of statistical and machine learning techniques for time series analysis and forecasting. The visualizations and models developed provide valuable insights into historical gold price trends and offer reliable predictions for future prices.
+
+Tools and Libraries:
+---------------------
+Programming Language: Python
+Libraries: Pandas, NumPy, Matplotlib, Seaborn, Statsmodels, Scikit-learn
+
+Future Work:
+--------------
+Incorporate external economic indicators to improve forecasting accuracy.
+Explore more advanced models such as ARIMA, SARIMA, and machine learning approaches like LSTM for time series forecasting.
+
+
diff --git a/Gold Price Prediction Tool/gold_monthly_csv (1) (1).csv b/Gold Price Prediction Tool/gold_monthly_csv (1) (1).csv
new file mode 100644
index 0000000000..851bfcd99e
--- /dev/null
+++ b/Gold Price Prediction Tool/gold_monthly_csv (1) (1).csv	
@@ -0,0 +1,848 @@
+Date,Price
+1950-01,34.730
+1950-02,34.730
+1950-03,34.730
+1950-04,34.730
+1950-05,34.730
+1950-06,34.730
+1950-07,34.730
+1950-08,34.730
+1950-09,34.730
+1950-10,34.730
+1950-11,34.730
+1950-12,34.720
+1951-01,34.720
+1951-02,34.730
+1951-03,34.730
+1951-04,34.730
+1951-05,34.730
+1951-06,34.730
+1951-07,34.720
+1951-08,34.710
+1951-09,34.710
+1951-10,34.710
+1951-11,34.730
+1951-12,34.660
+1952-01,34.490
+1952-02,34.490
+1952-03,34.680
+1952-04,34.820
+1952-05,34.730
+1952-06,34.530
+1952-07,34.570
+1952-08,34.580
+1952-09,34.510
+1952-10,34.630
+1952-11,34.720
+1952-12,34.790
+1953-01,34.880
+1953-02,34.950
+1953-03,34.920
+1953-04,34.930
+1953-05,34.900
+1953-06,34.890
+1953-07,34.910
+1953-08,34.910
+1953-09,34.760
+1953-10,34.790
+1953-11,34.860
+1953-12,34.850
+1954-01,34.860
+1954-02,34.880
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From 90f0aa021c7cb5c34c5bb0472cfffa5de2d5a61d Mon Sep 17 00:00:00 2001
From: Panchadip <165953910+Panchadip-128@users.noreply.github.com>
Date: Fri, 25 Oct 2024 01:23:25 +0530
Subject: [PATCH 3/8] Create wm_RL files

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 .../wm_RL files                                                  | 1 +
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+

From 19787a695f3159435f96e97c7dd5ad58fb5023b4 Mon Sep 17 00:00:00 2001
From: Panchadip <165953910+Panchadip-128@users.noreply.github.com>
Date: Fri, 25 Oct 2024 01:28:23 +0530
Subject: [PATCH 4/8] Add files via upload

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 .../README (15).md                            |   71 +
 ...Scores_in_Reinforcement_Learning (1).ipynb |  333 +
 .../Waste_Management_through_RL.ipynb         | 5596 +++++++++++++++++
 .../gitignore (3).txt                         |    7 +
 .../requirements (3).txt                      |    4 +
 .../untitled58 (1).py                         |  304 +
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 create mode 100644 Waste Management through Reinforcement Learning techniques/README (15).md
 create mode 100644 Waste Management through Reinforcement Learning techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning (1).ipynb
 create mode 100644 Waste Management through Reinforcement Learning techniques/Waste_Management_through_RL.ipynb
 create mode 100644 Waste Management through Reinforcement Learning techniques/gitignore (3).txt
 create mode 100644 Waste Management through Reinforcement Learning techniques/requirements (3).txt
 create mode 100644 Waste Management through Reinforcement Learning techniques/untitled58 (1).py

diff --git a/Waste Management through Reinforcement Learning techniques/README (15).md b/Waste Management through Reinforcement Learning techniques/README (15).md
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+# Project Overview
+Epsilon Explorer is a reinforcement learning project designed to explore and analyze the performance of an agent using an epsilon-greedy strategy. The primary objective of this project is to investigate how the agent learns over multiple episodes by balancing exploration (trying new actions) and exploitation (choosing the best-known actions). This project provides valuable insights into the agent's learning dynamics, highlighting the effects of epsilon decay on performance and score improvement.
+
+# Key Concepts
+Reinforcement Learning
+Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, allowing it to learn optimal strategies for maximizing cumulative rewards.
+
+# Epsilon-Greedy Strategy
+The epsilon-greedy strategy is a popular approach used in reinforcement learning to balance exploration and exploitation. The strategy employs a parameter, epsilon (ε), which determines the probability of choosing a random action (exploration) versus the best-known action (exploitation). As training progresses, epsilon typically decays, allowing the agent to rely more on learned knowledge and less on exploration.
+
+# Score Tracking
+Tracking the scores achieved by the agent during training provides valuable insights into its performance. By analyzing these scores, we can evaluate the effectiveness of different learning strategies and make informed decisions about tuning hyperparameters.
+
+
+![ep_RL2](https://github.com/user-attachments/assets/4b369c79-64ff-4600-b329-30df9552ad18)
+![epsilon_RL1](https://github.com/user-attachments/assets/8b577058-7e29-4776-9cb3-e9f8eaad2016)
+
+Further an real life application of this concept is implemented through the use of a Daily AI Waste Management technique 
+--------------------------------------------------------------------------------------------------------------------------
+
+## Project Documentation: Waste Management Reinforcement Learning
+
+Project Overview:
+------------------
+
+The project aims to develop a reinforcement learning (RL) agent to optimize waste collection in a simulated environment, minimizing overflow events and improving efficiency.
+1) Environment and State Representation:
+
+The state is represented by four features:
+Waste Level: Current waste level (0 to 1)
+Time of Day: A random value representing the time (0 to 24 hours)
+Weather Condition: A random value (0 to 1) indicating the weather
+Distance to Collection Point: A random value (0 to 10) representing the distance to the waste collection point.
+
+2) Action Space:
+
+The agent can choose between two actions:
+Wait (0): Do not collect waste.
+Collect Waste (1): Proceed with waste collection.
+
+3) Reward Structure:
+
+The reward system is designed to encourage efficient waste collection:
++10 for timely collection when the waste level exceeds the threshold.
+-5 for premature collection when the waste level is below the threshold.
+-1 for each time step to penalize waiting.
+
+4) Training Process:
+
+The agent is trained over 100 episodes, where each episode simulates a series of time steps (up to 20) where the agent makes decisions based on the current state.
+The agent learns from experience using a replay memory and updates its policy through Q-learning.
+
+5) Evaluation Metrics:
+
+Performance is evaluated using:
+Average Reward per Episode: Measures the effectiveness of the agent's actions.
+Epsilon Decay: Tracks the exploration rate, indicating how the agent balances exploration vs. exploitation.
+Overflow Events: Counts occurrences when the waste level exceeds the maximum capacity as per previous updation.
+
+6) Visualization:
+
+The results are visualized using Matplotlib to plot:
+Average rewards per episode, showing the agent's learning progression and rewards gained on successfull execution and implementation of a specified condition
+Epsilon decay over episodes, illustrating the shift from exploration to exploitation.
+Overflow events per episode, highlighting improvements in waste management techniques 
+
+Further the results have been visualized with the help of graphs:
+![use_case_Waste_management_RL1](https://github.com/user-attachments/assets/4d8724d1-c9d3-4d96-adf0-12b977398edd)
+![use_case_waste_managent_2](https://github.com/user-attachments/assets/7153c560-52fb-46c3-b3c0-62fcacb35247)
+![use_case_waste_management3](https://github.com/user-attachments/assets/1a8a7109-1c40-4286-bd05-c6cde101f355)
+
diff --git a/Waste Management through Reinforcement Learning techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning (1).ipynb b/Waste Management through Reinforcement Learning techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning (1).ipynb
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index 0000000000..e81023f810
--- /dev/null
+++ b/Waste Management through Reinforcement Learning techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning (1).ipynb	
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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5zt---XLb3Oj",
+        "outputId": "580db4bb-e9f7-47aa-cc1d-e5c9fd647d66"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/util.py:55: DeprecationWarning: jax.xla_computation is deprecated. Please use the AOT APIs; see https://jax.readthedocs.io/en/latest/aot.html. For example, replace xla_computation(f)(*xs) with jit(f).lower(*xs).compiler_ir('hlo'). See CHANGELOG.md for 0.4.30 for more examples.\n",
+            "  from jax import xla_computation as _xla_computation\n",
+            "/usr/local/lib/python3.10/dist-packages/gym/core.py:317: DeprecationWarning: \u001b[33mWARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
+            "  deprecation(\n",
+            "/usr/local/lib/python3.10/dist-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: \u001b[33mWARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
+            "  deprecation(\n",
+            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
+            "/usr/local/lib/python3.10/dist-packages/gym/utils/passive_env_checker.py:241: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n",
+            "  if not isinstance(terminated, (bool, np.bool8)):\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Episode: 1/100, Score: 41, Epsilon: 1.00\n",
+            "Episode: 2/100, Score: 13, Epsilon: 0.99\n",
+            "Episode: 3/100, Score: 10, Epsilon: 0.99\n",
+            "Episode: 4/100, Score: 38, Epsilon: 0.99\n",
+            "Episode: 5/100, Score: 24, Epsilon: 0.98\n",
+            "Episode: 6/100, Score: 20, Epsilon: 0.98\n",
+            "Episode: 7/100, Score: 9, Epsilon: 0.97\n",
+            "Episode: 8/100, Score: 10, Epsilon: 0.97\n",
+            "Episode: 9/100, Score: 9, Epsilon: 0.96\n",
+            "Episode: 10/100, Score: 10, Epsilon: 0.96\n",
+            "Episode: 11/100, Score: 21, Epsilon: 0.95\n",
+            "Episode: 12/100, Score: 17, Epsilon: 0.95\n",
+            "Episode: 13/100, Score: 9, Epsilon: 0.94\n",
+            "Episode: 14/100, Score: 35, Epsilon: 0.94\n",
+            "Episode: 15/100, Score: 30, Epsilon: 0.93\n",
+            "Episode: 16/100, Score: 11, Epsilon: 0.93\n",
+            "Episode: 17/100, Score: 15, Epsilon: 0.92\n",
+            "Episode: 18/100, Score: 13, Epsilon: 0.92\n",
+            "Episode: 19/100, Score: 17, Epsilon: 0.91\n",
+            "Episode: 20/100, Score: 14, Epsilon: 0.91\n",
+            "Episode: 21/100, Score: 19, Epsilon: 0.90\n",
+            "Episode: 22/100, Score: 31, Epsilon: 0.90\n",
+            "Episode: 23/100, Score: 13, Epsilon: 0.90\n",
+            "Episode: 24/100, Score: 22, Epsilon: 0.89\n",
+            "Episode: 25/100, Score: 13, Epsilon: 0.89\n",
+            "Episode: 26/100, Score: 13, Epsilon: 0.88\n",
+            "Episode: 27/100, Score: 11, Epsilon: 0.88\n",
+            "Episode: 28/100, Score: 24, Epsilon: 0.87\n",
+            "Episode: 29/100, Score: 33, Epsilon: 0.87\n",
+            "Episode: 30/100, Score: 8, Epsilon: 0.86\n",
+            "Episode: 31/100, Score: 12, Epsilon: 0.86\n",
+            "Episode: 32/100, Score: 68, Epsilon: 0.86\n",
+            "Episode: 33/100, Score: 23, Epsilon: 0.85\n",
+            "Episode: 34/100, Score: 9, Epsilon: 0.85\n",
+            "Episode: 35/100, Score: 11, Epsilon: 0.84\n",
+            "Episode: 36/100, Score: 12, Epsilon: 0.84\n",
+            "Episode: 37/100, Score: 17, Epsilon: 0.83\n",
+            "Episode: 38/100, Score: 12, Epsilon: 0.83\n",
+            "Episode: 39/100, Score: 22, Epsilon: 0.83\n",
+            "Episode: 40/100, Score: 26, Epsilon: 0.82\n",
+            "Episode: 41/100, Score: 27, Epsilon: 0.82\n",
+            "Episode: 42/100, Score: 33, Epsilon: 0.81\n",
+            "Episode: 43/100, Score: 39, Epsilon: 0.81\n",
+            "Episode: 44/100, Score: 10, Epsilon: 0.81\n",
+            "Episode: 45/100, Score: 52, Epsilon: 0.80\n",
+            "Episode: 46/100, Score: 13, Epsilon: 0.80\n",
+            "Episode: 47/100, Score: 23, Epsilon: 0.79\n",
+            "Episode: 48/100, Score: 18, Epsilon: 0.79\n",
+            "Episode: 49/100, Score: 20, Epsilon: 0.79\n",
+            "Episode: 50/100, Score: 23, Epsilon: 0.78\n",
+            "Episode: 51/100, Score: 18, Epsilon: 0.78\n",
+            "Episode: 52/100, Score: 40, Epsilon: 0.77\n",
+            "Episode: 53/100, Score: 18, Epsilon: 0.77\n",
+            "Episode: 54/100, Score: 22, Epsilon: 0.77\n",
+            "Episode: 55/100, Score: 11, Epsilon: 0.76\n",
+            "Episode: 56/100, Score: 11, Epsilon: 0.76\n",
+            "Episode: 57/100, Score: 27, Epsilon: 0.76\n",
+            "Episode: 58/100, Score: 18, Epsilon: 0.75\n",
+            "Episode: 59/100, Score: 11, Epsilon: 0.75\n",
+            "Episode: 60/100, Score: 30, Epsilon: 0.74\n",
+            "Episode: 61/100, Score: 21, Epsilon: 0.74\n",
+            "Episode: 62/100, Score: 19, Epsilon: 0.74\n",
+            "Episode: 63/100, Score: 35, Epsilon: 0.73\n",
+            "Episode: 64/100, Score: 34, Epsilon: 0.73\n",
+            "Episode: 65/100, Score: 16, Epsilon: 0.73\n",
+            "Episode: 66/100, Score: 116, Epsilon: 0.72\n",
+            "Episode: 67/100, Score: 14, Epsilon: 0.72\n",
+            "Episode: 68/100, Score: 19, Epsilon: 0.71\n",
+            "Episode: 69/100, Score: 29, Epsilon: 0.71\n",
+            "Episode: 70/100, Score: 24, Epsilon: 0.71\n",
+            "Episode: 71/100, Score: 31, Epsilon: 0.70\n",
+            "Episode: 72/100, Score: 14, Epsilon: 0.70\n",
+            "Episode: 73/100, Score: 27, Epsilon: 0.70\n",
+            "Episode: 74/100, Score: 16, Epsilon: 0.69\n",
+            "Episode: 75/100, Score: 46, Epsilon: 0.69\n",
+            "Episode: 76/100, Score: 12, Epsilon: 0.69\n",
+            "Episode: 77/100, Score: 10, Epsilon: 0.68\n",
+            "Episode: 78/100, Score: 15, Epsilon: 0.68\n",
+            "Episode: 79/100, Score: 11, Epsilon: 0.68\n",
+            "Episode: 80/100, Score: 10, Epsilon: 0.67\n",
+            "Episode: 81/100, Score: 15, Epsilon: 0.67\n",
+            "Episode: 82/100, Score: 21, Epsilon: 0.67\n",
+            "Episode: 83/100, Score: 16, Epsilon: 0.66\n",
+            "Episode: 84/100, Score: 10, Epsilon: 0.66\n",
+            "Episode: 85/100, Score: 46, Epsilon: 0.66\n",
+            "Episode: 86/100, Score: 59, Epsilon: 0.65\n",
+            "Episode: 87/100, Score: 31, Epsilon: 0.65\n",
+            "Episode: 88/100, Score: 50, Epsilon: 0.65\n",
+            "Episode: 89/100, Score: 57, Epsilon: 0.64\n",
+            "Episode: 90/100, Score: 45, Epsilon: 0.64\n",
+            "Episode: 91/100, Score: 31, Epsilon: 0.64\n",
+            "Episode: 92/100, Score: 14, Epsilon: 0.63\n",
+            "Episode: 93/100, Score: 38, Epsilon: 0.63\n",
+            "Episode: 94/100, Score: 25, Epsilon: 0.63\n",
+            "Episode: 95/100, Score: 16, Epsilon: 0.62\n",
+            "Episode: 96/100, Score: 45, Epsilon: 0.62\n",
+            "Episode: 97/100, Score: 20, Epsilon: 0.62\n",
+            "Episode: 98/100, Score: 28, Epsilon: 0.61\n",
+            "Episode: 99/100, Score: 14, Epsilon: 0.61\n",
+            "Episode: 100/100, Score: 17, Epsilon: 0.61\n"
+          ]
+        }
+      ],
+      "source": [
+        "import gym\n",
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras import models, layers, optimizers\n",
+        "import random\n",
+        "from collections import deque\n",
+        "\n",
+        "# CartPole environment\n",
+        "env = gym.make('CartPole-v1')\n",
+        "\n",
+        "# Hyperparameters\n",
+        "state_size = env.observation_space.shape[0]\n",
+        "action_size = env.action_space.n\n",
+        "learning_rate = 0.001\n",
+        "gamma = 0.95  # Discount factor for future rewards\n",
+        "epsilon = 1.0  # Exploration rate\n",
+        "epsilon_min = 0.01\n",
+        "epsilon_decay = 0.995\n",
+        "batch_size = 8\n",
+        "episodes = 100\n",
+        "\n",
+        "# Memory to store experiences (state, action, reward, next_state, done)\n",
+        "memory = deque(maxlen=200)  # Significantly reduced memory size\n",
+        "\n",
+        "#  build model\n",
+        "def build_model():\n",
+        "    model = models.Sequential()\n",
+        "    model.add(layers.Dense(8, input_dim=state_size, activation='relu'))  # Fewer neurons\n",
+        "    model.add(layers.Dense(8, activation='relu'))  # Fewer neurons\n",
+        "    model.add(layers.Dense(action_size, activation='linear'))\n",
+        "    model.compile(optimizer=optimizers.Adam(learning_rate=learning_rate), loss='mse')\n",
+        "    return model\n",
+        "\n",
+        "\n",
+        "model = build_model()\n",
+        "\n",
+        "# select action using epsilon-greedy strategy\n",
+        "def choose_action(state, epsilon):\n",
+        "    if np.random.rand() <= epsilon:\n",
+        "        return random.randrange(action_size)  # Exploration\n",
+        "    q_values = model.predict(state, verbose=0)\n",
+        "    return np.argmax(q_values[0])  # Exploitation\n",
+        "\n",
+        "#  replay training\n",
+        "def replay():\n",
+        "    if len(memory) < batch_size:\n",
+        "        return  # Not enough samples to train\n",
+        "\n",
+        "    minibatch = random.sample(memory, batch_size)\n",
+        "    states, targets = [], []\n",
+        "    for state, action, reward, next_state, done in minibatch:\n",
+        "        target = reward\n",
+        "        if not done:\n",
+        "            target += gamma * np.max(model.predict(next_state, verbose=0)[0])\n",
+        "        target_f = model.predict(state, verbose=0)\n",
+        "        target_f[0][action] = target\n",
+        "        states.append(state)\n",
+        "        targets.append(target_f)\n",
+        "\n",
+        "    # Train on the batch\n",
+        "    model.fit(np.vstack(states), np.vstack(targets), epochs=1, verbose=0)\n",
+        "\n",
+        "# Main loop\n",
+        "for e in range(episodes):\n",
+        "    state = env.reset()\n",
+        "    state = np.reshape(state, [1, state_size])\n",
+        "\n",
+        "    for time in range(200):\n",
+        "        action = choose_action(state, epsilon)\n",
+        "        next_state, reward, done, _ = env.step(action)\n",
+        "        next_state = np.reshape(next_state, [1, state_size])\n",
+        "\n",
+        "        # Modify the reward if the episode ends\n",
+        "        reward = reward if not done else -10\n",
+        "\n",
+        "        # Store experience in memory\n",
+        "        memory.append((state, action, reward, next_state, done))\n",
+        "\n",
+        "        # Move to the next state\n",
+        "        state = next_state\n",
+        "\n",
+        "        # End episode if done\n",
+        "        if done:\n",
+        "            print(f\"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {epsilon:.2f}\")\n",
+        "            break\n",
+        "\n",
+        "        # Training the model using replay memory\n",
+        "        replay()\n",
+        "\n",
+        "    # Decay epsilon\n",
+        "    if epsilon > epsilon_min:\n",
+        "        epsilon *= epsilon_decay\n",
+        "\n",
+        "env.close()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "\n",
+        "episodes = list(range(1, 101))\n",
+        "\n",
+        "\n",
+        "scores = [\n",
+        "    41, 13, 10, 38, 24, 20, 9, 10, 9, 10,\n",
+        "    21, 17, 9, 35, 30, 11, 15, 13, 17, 14,\n",
+        "    19, 31, 13, 22, 13, 13, 11, 24, 33, 8,\n",
+        "    12, 68, 23, 9, 11, 12, 17, 12, 22, 26,\n",
+        "    27, 33, 39, 10, 52, 13, 23, 18, 20, 23,\n",
+        "    18, 40, 18, 22, 11, 11, 27, 18, 11, 30,\n",
+        "    21, 19, 35, 34, 16, 116, 14, 19, 29, 24,\n",
+        "    31, 14, 27, 16, 46, 12, 10, 15, 11, 10,\n",
+        "    15, 21, 16, 10, 46, 59, 31, 50, 57, 45,\n",
+        "    31, 14, 38, 25, 16, 45, 20, 28, 14, 17\n",
+        "]\n",
+        "\n",
+        "epsilon_values = [\n",
+        "    1.00, 0.99, 0.99, 0.99, 0.98, 0.98, 0.97, 0.97, 0.96, 0.96,\n",
+        "    0.95, 0.95, 0.94, 0.94, 0.93, 0.93, 0.92, 0.92, 0.91, 0.91,\n",
+        "    0.90, 0.90, 0.90, 0.89, 0.89, 0.88, 0.88, 0.87, 0.87, 0.86,\n",
+        "    0.86, 0.86, 0.85, 0.85, 0.84, 0.84, 0.83, 0.83, 0.83, 0.82,\n",
+        "    0.82, 0.81, 0.81, 0.81, 0.80, 0.80, 0.79, 0.79, 0.79, 0.78,\n",
+        "    0.78, 0.77, 0.77, 0.77, 0.76, 0.76, 0.76, 0.75, 0.75, 0.74,\n",
+        "    0.74, 0.74, 0.73, 0.73, 0.73, 0.72, 0.72, 0.71, 0.71, 0.71,\n",
+        "    0.70, 0.70, 0.70, 0.69, 0.69, 0.69, 0.68, 0.68, 0.68, 0.67,\n",
+        "    0.67, 0.67, 0.66, 0.66, 0.66, 0.65, 0.65, 0.65, 0.64, 0.64,\n",
+        "    0.64, 0.63, 0.63, 0.63, 0.62, 0.62, 0.62, 0.61, 0.61, 0.61\n",
+        "]\n",
+        "\n",
+        "\n",
+        "plt.figure(figsize=(14, 6))\n",
+        "\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.plot(episodes, scores, marker='o', color='blue', label='Scores', linestyle='-', markersize=4)\n",
+        "plt.title('Scores per Episode')\n",
+        "plt.xlabel('Episodes')\n",
+        "plt.ylabel('Score')\n",
+        "plt.xticks(range(0, 101, 10))\n",
+        "plt.grid()\n",
+        "plt.legend()\n",
+        "\n",
+        "\n",
+        "plt.subplot(1, 2, 2)\n",
+        "plt.plot(episodes, epsilon_values, marker='x', color='orange', label='Epsilon', linestyle='--', markersize=4)\n",
+        "plt.title('Epsilon Decay over Episodes')\n",
+        "plt.xlabel('Episodes')\n",
+        "plt.ylabel('Epsilon')\n",
+        "plt.xticks(range(0, 101, 10))\n",
+        "plt.grid()\n",
+        "plt.legend()\n",
+        "\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 382
+        },
+        "id": "B5-ndEmzpuk1",
+        "outputId": "db3c4ac1-9ce2-436d-ab2c-c393e886ada8"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Waste Management through Reinforcement Learning techniques/Waste_Management_through_RL.ipynb b/Waste Management through Reinforcement Learning techniques/Waste_Management_through_RL.ipynb
new file mode 100644
index 0000000000..07143a83f0
--- /dev/null
+++ b/Waste Management through Reinforcement Learning techniques/Waste_Management_through_RL.ipynb	
@@ -0,0 +1,5596 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2-mm9PVGHDGK",
+        "outputId": "e5b79c87-3808-4be3-b43c-28154ce7302e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Episode: 1/100, Score: 21, Epsilon: 1.00\n",
+            "Episode: 2/100, Score: 21, Epsilon: 0.99\n",
+            "Episode: 3/100, Score: 21, Epsilon: 0.99\n",
+            "Episode: 4/100, Score: 21, Epsilon: 0.99\n",
+            "Episode: 5/100, Score: 21, Epsilon: 0.98\n",
+            "Episode: 6/100, Score: 21, Epsilon: 0.98\n",
+            "Episode: 7/100, Score: 21, Epsilon: 0.97\n",
+            "Episode: 8/100, Score: 21, Epsilon: 0.97\n",
+            "Episode: 9/100, Score: 21, Epsilon: 0.96\n",
+            "Episode: 10/100, Score: 21, Epsilon: 0.96\n",
+            "Episode: 11/100, Score: 21, Epsilon: 0.95\n",
+            "Episode: 12/100, Score: 21, Epsilon: 0.95\n",
+            "Episode: 13/100, Score: 21, Epsilon: 0.94\n",
+            "Episode: 14/100, Score: 21, Epsilon: 0.94\n",
+            "Episode: 15/100, Score: 21, Epsilon: 0.93\n",
+            "Episode: 16/100, Score: 21, Epsilon: 0.93\n",
+            "Episode: 17/100, Score: 21, Epsilon: 0.92\n",
+            "Episode: 18/100, Score: 21, Epsilon: 0.92\n",
+            "Episode: 19/100, Score: 21, Epsilon: 0.91\n",
+            "Episode: 20/100, Score: 21, Epsilon: 0.91\n",
+            "Episode: 21/100, Score: 21, Epsilon: 0.90\n",
+            "Episode: 22/100, Score: 21, Epsilon: 0.90\n",
+            "Episode: 23/100, Score: 21, Epsilon: 0.90\n",
+            "Episode: 24/100, Score: 21, Epsilon: 0.89\n",
+            "Episode: 25/100, Score: 21, Epsilon: 0.89\n",
+            "Episode: 26/100, Score: 21, Epsilon: 0.88\n",
+            "Episode: 27/100, Score: 21, Epsilon: 0.88\n",
+            "Episode: 28/100, Score: 21, Epsilon: 0.87\n",
+            "Episode: 29/100, Score: 21, Epsilon: 0.87\n",
+            "Episode: 30/100, Score: 21, Epsilon: 0.86\n",
+            "Episode: 31/100, Score: 21, Epsilon: 0.86\n",
+            "Episode: 32/100, Score: 21, Epsilon: 0.86\n",
+            "Episode: 33/100, Score: 21, Epsilon: 0.85\n",
+            "Episode: 34/100, Score: 21, Epsilon: 0.85\n",
+            "Episode: 35/100, Score: 21, Epsilon: 0.84\n",
+            "Episode: 36/100, Score: 21, Epsilon: 0.84\n",
+            "Episode: 37/100, Score: 21, Epsilon: 0.83\n",
+            "Episode: 38/100, Score: 21, Epsilon: 0.83\n",
+            "Episode: 39/100, Score: 21, Epsilon: 0.83\n",
+            "Episode: 40/100, Score: 21, Epsilon: 0.82\n",
+            "Episode: 41/100, Score: 21, Epsilon: 0.82\n",
+            "Episode: 42/100, Score: 21, Epsilon: 0.81\n",
+            "Episode: 43/100, Score: 21, Epsilon: 0.81\n",
+            "Episode: 44/100, Score: 21, Epsilon: 0.81\n",
+            "Episode: 45/100, Score: 21, Epsilon: 0.80\n",
+            "Episode: 46/100, Score: 21, Epsilon: 0.80\n",
+            "Episode: 47/100, Score: 21, Epsilon: 0.79\n",
+            "Episode: 48/100, Score: 21, Epsilon: 0.79\n",
+            "Episode: 49/100, Score: 21, Epsilon: 0.79\n",
+            "Episode: 50/100, Score: 21, Epsilon: 0.78\n",
+            "Episode: 51/100, Score: 21, Epsilon: 0.78\n",
+            "Episode: 52/100, Score: 21, Epsilon: 0.77\n",
+            "Episode: 53/100, Score: 21, Epsilon: 0.77\n",
+            "Episode: 54/100, Score: 21, Epsilon: 0.77\n",
+            "Episode: 55/100, Score: 21, Epsilon: 0.76\n",
+            "Episode: 56/100, Score: 21, Epsilon: 0.76\n",
+            "Episode: 57/100, Score: 21, Epsilon: 0.76\n",
+            "Episode: 58/100, Score: 21, Epsilon: 0.75\n",
+            "Episode: 59/100, Score: 21, Epsilon: 0.75\n",
+            "Episode: 60/100, Score: 21, Epsilon: 0.74\n",
+            "Episode: 61/100, Score: 21, Epsilon: 0.74\n",
+            "Episode: 62/100, Score: 21, Epsilon: 0.74\n",
+            "Episode: 63/100, Score: 21, Epsilon: 0.73\n",
+            "Episode: 64/100, Score: 21, Epsilon: 0.73\n",
+            "Episode: 65/100, Score: 21, Epsilon: 0.73\n",
+            "Episode: 66/100, Score: 21, Epsilon: 0.72\n",
+            "Episode: 67/100, Score: 21, Epsilon: 0.72\n",
+            "Episode: 68/100, Score: 21, Epsilon: 0.71\n",
+            "Episode: 69/100, Score: 21, Epsilon: 0.71\n",
+            "Episode: 70/100, Score: 21, Epsilon: 0.71\n",
+            "Episode: 71/100, Score: 21, Epsilon: 0.70\n",
+            "Episode: 72/100, Score: 21, Epsilon: 0.70\n",
+            "Episode: 73/100, Score: 21, Epsilon: 0.70\n",
+            "Episode: 74/100, Score: 21, Epsilon: 0.69\n",
+            "Episode: 75/100, Score: 21, Epsilon: 0.69\n",
+            "Episode: 76/100, Score: 21, Epsilon: 0.69\n",
+            "Episode: 77/100, Score: 21, Epsilon: 0.68\n",
+            "Episode: 78/100, Score: 21, Epsilon: 0.68\n",
+            "Episode: 79/100, Score: 21, Epsilon: 0.68\n",
+            "Episode: 80/100, Score: 21, Epsilon: 0.67\n",
+            "Episode: 81/100, Score: 21, Epsilon: 0.67\n",
+            "Episode: 82/100, Score: 21, Epsilon: 0.67\n",
+            "Episode: 83/100, Score: 21, Epsilon: 0.66\n",
+            "Episode: 84/100, Score: 21, Epsilon: 0.66\n",
+            "Episode: 85/100, Score: 21, Epsilon: 0.66\n",
+            "Episode: 86/100, Score: 21, Epsilon: 0.65\n",
+            "Episode: 87/100, Score: 21, Epsilon: 0.65\n",
+            "Episode: 88/100, Score: 21, Epsilon: 0.65\n",
+            "Episode: 89/100, Score: 21, Epsilon: 0.64\n",
+            "Episode: 90/100, Score: 21, Epsilon: 0.64\n",
+            "Episode: 91/100, Score: 21, Epsilon: 0.64\n",
+            "Episode: 92/100, Score: 21, Epsilon: 0.63\n",
+            "Episode: 93/100, Score: 21, Epsilon: 0.63\n",
+            "Episode: 94/100, Score: 21, Epsilon: 0.63\n",
+            "Episode: 95/100, Score: 21, Epsilon: 0.62\n",
+            "Episode: 96/100, Score: 21, Epsilon: 0.62\n",
+            "Episode: 97/100, Score: 21, Epsilon: 0.62\n",
+            "Episode: 98/100, Score: 21, Epsilon: 0.61\n",
+            "Episode: 99/100, Score: 21, Epsilon: 0.61\n",
+            "Episode: 100/100, Score: 21, Epsilon: 0.61\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import random\n",
+        "from collections import deque\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "\n",
+        "class EnergyManagementAgent:\n",
+        "    def __init__(self, state_size, action_size):\n",
+        "        self.state_size = state_size\n",
+        "        self.action_size = action_size\n",
+        "        self.memory = deque(maxlen=2000)\n",
+        "        self.gamma = 0.95  # discount rate\n",
+        "        self.epsilon = 1.0  # exploration rate\n",
+        "        self.epsilon_min = 0.01\n",
+        "        self.epsilon_decay = 0.995\n",
+        "        self.learning_rate = 0.001\n",
+        "        self.model = self._build_model()\n",
+        "\n",
+        "    def _build_model(self):\n",
+        "        model = Sequential()\n",
+        "        model.add(tf.keras.Input(shape=(self.state_size,)))\n",
+        "        model.add(Dense(24, activation='relu'))\n",
+        "        model.add(Dense(24, activation='relu'))\n",
+        "        model.add(Dense(self.action_size, activation='linear'))\n",
+        "        model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate))\n",
+        "        return model\n",
+        "\n",
+        "    def remember(self, state, action, reward, next_state, done):\n",
+        "        self.memory.append((state, action, reward, next_state, done))\n",
+        "\n",
+        "    def act(self, state):\n",
+        "        if np.random.rand() <= self.epsilon:\n",
+        "            return random.randrange(self.action_size)\n",
+        "        act_values = self.model.predict(state, verbose=0)\n",
+        "        return np.argmax(act_values[0])\n",
+        "\n",
+        "    def replay(self, batch_size):\n",
+        "        minibatch = random.sample(self.memory, batch_size)\n",
+        "        for state, action, reward, next_state, done in minibatch:\n",
+        "            target = reward\n",
+        "            if not done:\n",
+        "                target = reward + self.gamma * np.amax(self.model.predict(next_state, verbose=0)[0])\n",
+        "            target_f = self.model.predict(state, verbose=0)\n",
+        "            target_f[0][action] = target\n",
+        "            self.model.fit(state, target_f, epochs=1, verbose=0)\n",
+        "        if self.epsilon > self.epsilon_min:\n",
+        "            self.epsilon *= self.epsilon_decay\n",
+        "\n",
+        "    def load(self, name):\n",
+        "        self.model.load_weights(name)\n",
+        "\n",
+        "    def save(self, name):\n",
+        "        self.model.save_weights(name)\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    # Environment parameters\n",
+        "    state_size = 4  # Example state: [current energy usage, time of day, temperature, price]\n",
+        "    action_size = 2  # Example actions: [0: reduce usage, 1: maintain usage]\n",
+        "    agent = EnergyManagementAgent(state_size, action_size)\n",
+        "    episodes = 100\n",
+        "\n",
+        "    # Train the agent in the environment\n",
+        "    for e in range(episodes):\n",
+        "        # Reset environment for each episode\n",
+        "        state = np.reshape(np.random.rand(state_size), [1, state_size])\n",
+        "        done = False\n",
+        "        time = 0\n",
+        "\n",
+        "        while not done:\n",
+        "            # Take action\n",
+        "            action = agent.act(state)\n",
+        "            # Simulate next state and reward (replace with environment logic)\n",
+        "            next_state = np.reshape(np.random.rand(state_size), [1, state_size])\n",
+        "            reward = random.uniform(-1, 1)  # Example reward\n",
+        "            done = time >= 20  # Example end condition\n",
+        "\n",
+        "            # Remember the experience\n",
+        "            agent.remember(state, action, reward, next_state, done)\n",
+        "\n",
+        "            # Move to the next state\n",
+        "            state = next_state\n",
+        "            time += 1\n",
+        "\n",
+        "        # Replay experience to train the model\n",
+        "        if len(agent.memory) > 32:\n",
+        "            agent.replay(32)\n",
+        "\n",
+        "        # Print progress\n",
+        "        print(f\"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {agent.epsilon:.2f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ky6lNNgvbFSW"
+      },
+      "outputs": [],
+      "source": [
+        "state_size = 4  # Example state: [waste level, time of day, weather, distance to collection point]\n",
+        "action_size = 2  # Example actions: [0: wait, 1: collect waste]\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YYrGz8uTbGBq",
+        "outputId": "573c1a02-0ad1-4d60-a503-b80bc49bb885"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Episode: 1/100, Score: 21, Epsilon: 1.00, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 2/100, Score: 21, Epsilon: 0.99, Overflow Events: 0, Average Reward: -2.76\n",
+            "Episode: 3/100, Score: 21, Epsilon: 0.99, Overflow Events: 0, Average Reward: -2.05\n",
+            "Episode: 4/100, Score: 21, Epsilon: 0.99, Overflow Events: 0, Average Reward: -2.38\n",
+            "Episode: 5/100, Score: 21, Epsilon: 0.98, Overflow Events: 0, Average Reward: -1.62\n",
+            "Episode: 6/100, Score: 21, Epsilon: 0.98, Overflow Events: 0, Average Reward: -1.05\n",
+            "Episode: 7/100, Score: 21, Epsilon: 0.97, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 8/100, Score: 21, Epsilon: 0.97, Overflow Events: 0, Average Reward: -1.62\n",
+            "Episode: 9/100, Score: 21, Epsilon: 0.96, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 10/100, Score: 21, Epsilon: 0.96, Overflow Events: 0, Average Reward: -2.76\n",
+            "Episode: 11/100, Score: 21, Epsilon: 0.95, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 12/100, Score: 21, Epsilon: 0.95, Overflow Events: 0, Average Reward: -1.62\n",
+            "Episode: 13/100, Score: 21, Epsilon: 0.94, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 14/100, Score: 21, Epsilon: 0.94, Overflow Events: 0, Average Reward: -1.10\n",
+            "Episode: 15/100, Score: 21, Epsilon: 0.93, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 16/100, Score: 21, Epsilon: 0.93, Overflow Events: 0, Average Reward: -2.57\n",
+            "Episode: 17/100, Score: 21, Epsilon: 0.92, Overflow Events: 4, Average Reward: -1.24\n",
+            "Episode: 18/100, Score: 21, Epsilon: 0.92, Overflow Events: 0, Average Reward: -2.95\n",
+            "Episode: 19/100, Score: 21, Epsilon: 0.91, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 20/100, Score: 21, Epsilon: 0.91, Overflow Events: 0, Average Reward: -1.62\n",
+            "Episode: 21/100, Score: 21, Epsilon: 0.90, Overflow Events: 0, Average Reward: -1.24\n",
+            "Episode: 22/100, Score: 21, Epsilon: 0.90, Overflow Events: 0, Average Reward: -1.43\n",
+            "Episode: 23/100, Score: 21, Epsilon: 0.90, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 24/100, Score: 21, Epsilon: 0.89, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 25/100, Score: 21, Epsilon: 0.89, Overflow Events: 0, Average Reward: -2.38\n",
+            "Episode: 26/100, Score: 21, Epsilon: 0.88, Overflow Events: 0, Average Reward: -1.62\n",
+            "Episode: 27/100, Score: 21, Epsilon: 0.88, Overflow Events: 0, Average Reward: -1.24\n",
+            "Episode: 28/100, Score: 21, Epsilon: 0.87, Overflow Events: 0, Average Reward: -2.76\n",
+            "Episode: 29/100, Score: 21, Epsilon: 0.87, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 30/100, Score: 21, Epsilon: 0.86, Overflow Events: 0, Average Reward: -2.57\n",
+            "Episode: 31/100, Score: 21, Epsilon: 0.86, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 32/100, Score: 21, Epsilon: 0.86, Overflow Events: 0, Average Reward: -2.57\n",
+            "Episode: 33/100, Score: 21, Epsilon: 0.85, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 34/100, Score: 21, Epsilon: 0.85, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 35/100, Score: 21, Epsilon: 0.84, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 36/100, Score: 21, Epsilon: 0.84, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 37/100, Score: 21, Epsilon: 0.83, Overflow Events: 0, Average Reward: -2.05\n",
+            "Episode: 38/100, Score: 21, Epsilon: 0.83, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 39/100, Score: 21, Epsilon: 0.83, Overflow Events: 0, Average Reward: -2.38\n",
+            "Episode: 40/100, Score: 21, Epsilon: 0.82, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 41/100, Score: 21, Epsilon: 0.82, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 42/100, Score: 21, Epsilon: 0.81, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 43/100, Score: 21, Epsilon: 0.81, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 44/100, Score: 21, Epsilon: 0.81, Overflow Events: 1, Average Reward: -1.81\n",
+            "Episode: 45/100, Score: 21, Epsilon: 0.80, Overflow Events: 0, Average Reward: -2.57\n",
+            "Episode: 46/100, Score: 21, Epsilon: 0.80, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 47/100, Score: 21, Epsilon: 0.79, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 48/100, Score: 21, Epsilon: 0.79, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 49/100, Score: 21, Epsilon: 0.79, Overflow Events: 0, Average Reward: -1.62\n",
+            "Episode: 50/100, Score: 21, Epsilon: 0.78, Overflow Events: 0, Average Reward: -1.67\n",
+            "Episode: 51/100, Score: 21, Epsilon: 0.78, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 52/100, Score: 21, Epsilon: 0.77, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 53/100, Score: 21, Epsilon: 0.77, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 54/100, Score: 21, Epsilon: 0.77, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 55/100, Score: 21, Epsilon: 0.76, Overflow Events: 0, Average Reward: -2.76\n",
+            "Episode: 56/100, Score: 21, Epsilon: 0.76, Overflow Events: 0, Average Reward: -2.76\n",
+            "Episode: 57/100, Score: 21, Epsilon: 0.76, Overflow Events: 0, Average Reward: -2.57\n",
+            "Episode: 58/100, Score: 21, Epsilon: 0.75, Overflow Events: 0, Average Reward: -1.62\n",
+            "Episode: 59/100, Score: 21, Epsilon: 0.75, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 60/100, Score: 21, Epsilon: 0.74, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 61/100, Score: 21, Epsilon: 0.74, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 62/100, Score: 21, Epsilon: 0.74, Overflow Events: 0, Average Reward: -2.38\n",
+            "Episode: 63/100, Score: 21, Epsilon: 0.73, Overflow Events: 0, Average Reward: -1.24\n",
+            "Episode: 64/100, Score: 21, Epsilon: 0.73, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 65/100, Score: 21, Epsilon: 0.73, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 66/100, Score: 21, Epsilon: 0.72, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 67/100, Score: 21, Epsilon: 0.72, Overflow Events: 0, Average Reward: -0.71\n",
+            "Episode: 68/100, Score: 21, Epsilon: 0.71, Overflow Events: 0, Average Reward: -1.62\n",
+            "Episode: 69/100, Score: 21, Epsilon: 0.71, Overflow Events: 0, Average Reward: -0.33\n",
+            "Episode: 70/100, Score: 21, Epsilon: 0.71, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 71/100, Score: 21, Epsilon: 0.70, Overflow Events: 0, Average Reward: -1.43\n",
+            "Episode: 72/100, Score: 21, Epsilon: 0.70, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 73/100, Score: 21, Epsilon: 0.70, Overflow Events: 0, Average Reward: -1.62\n",
+            "Episode: 74/100, Score: 21, Epsilon: 0.69, Overflow Events: 0, Average Reward: -1.62\n",
+            "Episode: 75/100, Score: 21, Epsilon: 0.69, Overflow Events: 0, Average Reward: -1.62\n",
+            "Episode: 76/100, Score: 21, Epsilon: 0.69, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 77/100, Score: 21, Epsilon: 0.68, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 78/100, Score: 21, Epsilon: 0.68, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 79/100, Score: 21, Epsilon: 0.68, Overflow Events: 0, Average Reward: -1.24\n",
+            "Episode: 80/100, Score: 21, Epsilon: 0.67, Overflow Events: 0, Average Reward: -1.43\n",
+            "Episode: 81/100, Score: 21, Epsilon: 0.67, Overflow Events: 0, Average Reward: -0.86\n",
+            "Episode: 82/100, Score: 21, Epsilon: 0.67, Overflow Events: 0, Average Reward: -2.38\n",
+            "Episode: 83/100, Score: 21, Epsilon: 0.66, Overflow Events: 0, Average Reward: -1.43\n",
+            "Episode: 84/100, Score: 21, Epsilon: 0.66, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 85/100, Score: 21, Epsilon: 0.66, Overflow Events: 0, Average Reward: -1.24\n",
+            "Episode: 86/100, Score: 21, Epsilon: 0.65, Overflow Events: 0, Average Reward: -1.62\n",
+            "Episode: 87/100, Score: 21, Epsilon: 0.65, Overflow Events: 0, Average Reward: -1.81\n",
+            "Episode: 88/100, Score: 21, Epsilon: 0.65, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 89/100, Score: 21, Epsilon: 0.64, Overflow Events: 0, Average Reward: -1.05\n",
+            "Episode: 90/100, Score: 21, Epsilon: 0.64, Overflow Events: 0, Average Reward: -1.05\n",
+            "Episode: 91/100, Score: 21, Epsilon: 0.64, Overflow Events: 0, Average Reward: -0.71\n",
+            "Episode: 92/100, Score: 21, Epsilon: 0.63, Overflow Events: 0, Average Reward: -2.19\n",
+            "Episode: 93/100, Score: 21, Epsilon: 0.63, Overflow Events: 0, Average Reward: -1.24\n",
+            "Episode: 94/100, Score: 21, Epsilon: 0.63, Overflow Events: 0, Average Reward: -0.86\n",
+            "Episode: 95/100, Score: 21, Epsilon: 0.62, Overflow Events: 0, Average Reward: -2.38\n",
+            "Episode: 96/100, Score: 21, Epsilon: 0.62, Overflow Events: 1, Average Reward: -1.24\n",
+            "Episode: 97/100, Score: 21, Epsilon: 0.62, Overflow Events: 0, Average Reward: -2.00\n",
+            "Episode: 98/100, Score: 21, Epsilon: 0.61, Overflow Events: 6, Average Reward: -0.86\n",
+            "Episode: 99/100, Score: 21, Epsilon: 0.61, Overflow Events: 0, Average Reward: -0.86\n",
+            "Episode: 100/100, Score: 21, Epsilon: 0.61, Overflow Events: 0, Average Reward: -0.71\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Environment parameters\n",
+        "state_size = 4  # Example state: [waste level, time of day, weather, distance to collection point]\n",
+        "action_size = 2  # Example actions: [0: wait, 1: collect waste]\n",
+        "agent = EnergyManagementAgent(state_size, action_size)\n",
+        "episodes = 100\n",
+        "\n",
+        "# Waste management specific parameters\n",
+        "threshold = 0.7  # Waste level threshold (between 0 and 1, where 1 means full capacity)\n",
+        "\n",
+        "# Train the agent in the environment\n",
+        "for e in range(episodes):\n",
+        "    # Reset environment for each episode\n",
+        "    waste_level = random.uniform(0, 0.5)  # Start with a random waste level below the threshold\n",
+        "    state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size])\n",
+        "    done = False\n",
+        "    time = 0\n",
+        "    overflow_count = 0  # Track overflow events\n",
+        "    rewards = []\n",
+        "\n",
+        "    while not done:\n",
+        "        # Take action\n",
+        "        action = agent.act(state)\n",
+        "        # Simulate next state and reward (replace with environment logic)\n",
+        "        waste_level += random.uniform(0, 0.1)  # Waste increases over time\n",
+        "        if waste_level > 1.0:  # Overflow occurred\n",
+        "            overflow_count += 1\n",
+        "            waste_level = 1.0\n",
+        "\n",
+        "        next_state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size])\n",
+        "\n",
+        "        # Reward structure\n",
+        "        reward = -1  # Default reward for time passing\n",
+        "        if waste_level > threshold and action == 1:\n",
+        "            reward = 10  # Positive reward for timely collection\n",
+        "            waste_level = 0  # Waste collected\n",
+        "        elif waste_level < threshold and action == 1:\n",
+        "            reward = -5  # Penalty for collecting too early\n",
+        "\n",
+        "        rewards.append(reward)\n",
+        "\n",
+        "        done = time >= 20  # Example end condition\n",
+        "\n",
+        "        # Remember the experience\n",
+        "        agent.remember(state, action, reward, next_state, done)\n",
+        "\n",
+        "        # Move to the next state\n",
+        "        state = next_state\n",
+        "        time += 1\n",
+        "\n",
+        "    # Replay experience to train the model\n",
+        "    if len(agent.memory) > 32:\n",
+        "        agent.replay(32)\n",
+        "\n",
+        "    # Print progress\n",
+        "    print(f\"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {agent.epsilon:.2f}, Overflow Events: {overflow_count}, Average Reward: {np.mean(rewards):.2f}\")\n",
+        "plt.figure(figsize=(15, 5))\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Q6xdPil6fQ7M",
+        "outputId": "6cf5f96c-a5d3-4c3f-9f9a-a5eceaeec02c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "Episode: 28/100, Score: 21, Epsilon: 0.87, Overflow Events: 0, Average Reward: -2.38\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "Episode: 29/100, Score: 21, Epsilon: 0.87, Overflow Events: 0, Average Reward: -2.00\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "Episode: 30/100, Score: 21, Epsilon: 0.86, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "Episode: 31/100, Score: 21, Epsilon: 0.86, Overflow Events: 0, Average Reward: -2.76\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "Episode: 32/100, Score: 21, Epsilon: 0.86, Overflow Events: 0, Average Reward: -2.38\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "Episode: 33/100, Score: 21, Epsilon: 0.85, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "Episode: 34/100, Score: 21, Epsilon: 0.85, Overflow Events: 0, Average Reward: -2.00\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "Episode: 35/100, Score: 21, Epsilon: 0.84, Overflow Events: 0, Average Reward: -1.86\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "Episode: 36/100, Score: 21, Epsilon: 0.84, Overflow Events: 0, Average Reward: -2.38\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "Episode: 37/100, Score: 21, Epsilon: 0.83, Overflow Events: 0, Average Reward: -2.00\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "Episode: 38/100, Score: 21, Epsilon: 0.83, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "Episode: 39/100, Score: 21, Epsilon: 0.83, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "Episode: 40/100, Score: 21, Epsilon: 0.82, Overflow Events: 0, Average Reward: -1.62\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "Episode: 41/100, Score: 21, Epsilon: 0.82, Overflow Events: 0, Average Reward: -1.67\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "Episode: 42/100, Score: 21, Epsilon: 0.81, Overflow Events: 0, Average Reward: -2.00\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "Episode: 43/100, Score: 21, Epsilon: 0.81, Overflow Events: 0, Average Reward: -2.19\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "Episode: 44/100, Score: 21, Epsilon: 0.81, Overflow Events: 0, Average Reward: -1.62\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "Episode: 45/100, Score: 21, Epsilon: 0.80, Overflow Events: 0, Average Reward: -2.19\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "Episode: 46/100, Score: 21, Epsilon: 0.80, Overflow Events: 0, Average Reward: -2.00\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "Episode: 47/100, Score: 21, Epsilon: 0.79, Overflow Events: 0, Average Reward: -2.00\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "Episode: 48/100, Score: 21, Epsilon: 0.79, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "Episode: 49/100, Score: 21, Epsilon: 0.79, Overflow Events: 0, Average Reward: -2.19\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "Episode: 50/100, Score: 21, Epsilon: 0.78, Overflow Events: 0, Average Reward: -2.00\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "Episode: 51/100, Score: 21, Epsilon: 0.78, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "Episode: 52/100, Score: 21, Epsilon: 0.77, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "Episode: 53/100, Score: 21, Epsilon: 0.77, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
+            "Episode: 54/100, Score: 21, Epsilon: 0.77, Overflow Events: 0, Average Reward: -1.62\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "Episode: 55/100, Score: 21, Epsilon: 0.76, Overflow Events: 0, Average Reward: -2.00\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "Episode: 56/100, Score: 21, Epsilon: 0.76, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "Episode: 57/100, Score: 21, Epsilon: 0.76, Overflow Events: 0, Average Reward: -2.19\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "Episode: 58/100, Score: 21, Epsilon: 0.75, Overflow Events: 0, Average Reward: -2.00\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "Episode: 59/100, Score: 21, Epsilon: 0.75, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "Episode: 60/100, Score: 21, Epsilon: 0.74, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "Episode: 61/100, Score: 21, Epsilon: 0.74, Overflow Events: 0, Average Reward: -1.62\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "Episode: 62/100, Score: 21, Epsilon: 0.74, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "Episode: 63/100, Score: 21, Epsilon: 0.73, Overflow Events: 0, Average Reward: -1.62\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "Episode: 64/100, Score: 21, Epsilon: 0.73, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "Episode: 65/100, Score: 21, Epsilon: 0.73, Overflow Events: 0, Average Reward: -2.57\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "Episode: 66/100, Score: 21, Epsilon: 0.72, Overflow Events: 0, Average Reward: -1.62\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "Episode: 67/100, Score: 21, Epsilon: 0.72, Overflow Events: 0, Average Reward: -1.62\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "Episode: 68/100, Score: 21, Epsilon: 0.71, Overflow Events: 0, Average Reward: -1.67\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "Episode: 69/100, Score: 21, Epsilon: 0.71, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "Episode: 70/100, Score: 21, Epsilon: 0.71, Overflow Events: 0, Average Reward: -2.19\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "Episode: 71/100, Score: 21, Epsilon: 0.70, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "Episode: 72/100, Score: 21, Epsilon: 0.70, Overflow Events: 0, Average Reward: -1.62\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "Episode: 73/100, Score: 21, Epsilon: 0.70, Overflow Events: 0, Average Reward: -1.05\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "Episode: 74/100, Score: 21, Epsilon: 0.69, Overflow Events: 0, Average Reward: -1.62\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "Episode: 75/100, Score: 21, Epsilon: 0.69, Overflow Events: 0, Average Reward: -2.38\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "Episode: 76/100, Score: 21, Epsilon: 0.69, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "Episode: 77/100, Score: 21, Epsilon: 0.68, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
+            "Episode: 78/100, Score: 21, Epsilon: 0.68, Overflow Events: 0, Average Reward: -2.00\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "Episode: 79/100, Score: 21, Epsilon: 0.68, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "Episode: 80/100, Score: 21, Epsilon: 0.67, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "Episode: 81/100, Score: 21, Epsilon: 0.67, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "Episode: 82/100, Score: 21, Epsilon: 0.67, Overflow Events: 0, Average Reward: -2.38\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "Episode: 83/100, Score: 21, Epsilon: 0.66, Overflow Events: 0, Average Reward: -1.05\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "Episode: 84/100, Score: 21, Epsilon: 0.66, Overflow Events: 4, Average Reward: -0.67\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "Episode: 85/100, Score: 21, Epsilon: 0.66, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "Episode: 86/100, Score: 21, Epsilon: 0.65, Overflow Events: 0, Average Reward: -2.19\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "Episode: 87/100, Score: 21, Epsilon: 0.65, Overflow Events: 0, Average Reward: -1.62\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "Episode: 88/100, Score: 21, Epsilon: 0.65, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "Episode: 89/100, Score: 21, Epsilon: 0.64, Overflow Events: 0, Average Reward: -1.29\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step\n",
+            "Episode: 90/100, Score: 21, Epsilon: 0.64, Overflow Events: 0, Average Reward: -1.05\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "Episode: 91/100, Score: 21, Epsilon: 0.64, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "Episode: 92/100, Score: 21, Epsilon: 0.63, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "Episode: 93/100, Score: 21, Epsilon: 0.63, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "Episode: 94/100, Score: 21, Epsilon: 0.63, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "Episode: 95/100, Score: 21, Epsilon: 0.62, Overflow Events: 0, Average Reward: -1.81\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "Episode: 96/100, Score: 21, Epsilon: 0.62, Overflow Events: 5, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "Episode: 97/100, Score: 21, Epsilon: 0.62, Overflow Events: 0, Average Reward: -1.05\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "Episode: 98/100, Score: 21, Epsilon: 0.61, Overflow Events: 0, Average Reward: -1.24\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "Episode: 99/100, Score: 21, Epsilon: 0.61, Overflow Events: 0, Average Reward: -1.43\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
+            "Episode: 100/100, Score: 21, Epsilon: 0.61, Overflow Events: 0, Average Reward: -1.43\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1500x500 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import random\n",
+        "from collections import deque\n",
+        "import matplotlib.pyplot as plt\n",
+        "from keras.models import Sequential\n",
+        "from keras.layers import Dense\n",
+        "from keras.optimizers import Adam\n",
+        "\n",
+        "class EnergyManagementAgent:\n",
+        "    def __init__(self, state_size, action_size):\n",
+        "        self.state_size = state_size\n",
+        "        self.action_size = action_size\n",
+        "        self.memory = deque(maxlen=2000)\n",
+        "        self.gamma = 0.95    # discount rate\n",
+        "        self.epsilon = 1.0   # exploration rate\n",
+        "        self.epsilon_min = 0.01\n",
+        "        self.epsilon_decay = 0.995\n",
+        "        self.learning_rate = 0.001\n",
+        "        self.model = self._build_model()\n",
+        "\n",
+        "    def _build_model(self):\n",
+        "        model = Sequential()\n",
+        "        model.add(Dense(24, input_dim=self.state_size, activation='relu'))\n",
+        "        model.add(Dense(24, activation='relu'))\n",
+        "        model.add(Dense(self.action_size, activation='linear'))\n",
+        "        # Update learning rate parameter\n",
+        "        model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate))\n",
+        "        return model\n",
+        "\n",
+        "    def remember(self, state, action, reward, next_state, done):\n",
+        "        self.memory.append((state, action, reward, next_state, done))\n",
+        "\n",
+        "    def act(self, state):\n",
+        "        if np.random.rand() <= self.epsilon:\n",
+        "            return random.randrange(self.action_size)\n",
+        "        act_values = self.model.predict(state)\n",
+        "        return np.argmax(act_values[0])\n",
+        "\n",
+        "    def replay(self, batch_size):\n",
+        "        minibatch = random.sample(self.memory, batch_size)\n",
+        "        for state, action, reward, next_state, done in minibatch:\n",
+        "            target = reward\n",
+        "            if not done:\n",
+        "                target = (reward + self.gamma *\n",
+        "                          np.amax(self.model.predict(next_state)[0]))\n",
+        "            target_f = self.model.predict(state)\n",
+        "            target_f[0][action] = target\n",
+        "            self.model.fit(state, target_f, epochs=1, verbose=0)\n",
+        "        if self.epsilon > self.epsilon_min:\n",
+        "            self.epsilon *= self.epsilon_decay\n",
+        "\n",
+        "# Environment parameters\n",
+        "state_size = 4  # Example state: [waste level, time of day, weather, distance to collection point]\n",
+        "action_size = 2  # Example actions: [0: wait, 1: collect waste]\n",
+        "agent = EnergyManagementAgent(state_size, action_size)\n",
+        "episodes = 100\n",
+        "\n",
+        "# Waste management specific parameters\n",
+        "threshold = 0.7  # Waste level threshold (between 0 and 1, where 1 means full capacity)\n",
+        "\n",
+        "# Tracking metrics\n",
+        "episode_rewards = []\n",
+        "epsilon_values = []\n",
+        "overflow_events_per_episode = []\n",
+        "\n",
+        "# Train the agent in the environment\n",
+        "for e in range(episodes):\n",
+        "    # Reset environment for each episode\n",
+        "    waste_level = random.uniform(0, 0.5)  # Start with a random waste level below the threshold\n",
+        "    state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size])\n",
+        "    done = False\n",
+        "    time = 0\n",
+        "    overflow_count = 0  # Track overflow events\n",
+        "    rewards = []\n",
+        "\n",
+        "    while not done:\n",
+        "        # Take action\n",
+        "        action = agent.act(state)\n",
+        "        # Simulate next state and reward (replace with environment logic)\n",
+        "        waste_level += random.uniform(0, 0.1)  # Waste increases over time\n",
+        "        if waste_level > 1.0:  # Overflow occurred\n",
+        "            overflow_count += 1\n",
+        "            waste_level = 1.0\n",
+        "\n",
+        "        next_state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size])\n",
+        "\n",
+        "        # Reward structure\n",
+        "        reward = -1  # Default reward for time passing\n",
+        "        if waste_level > threshold and action == 1:\n",
+        "            reward = 10  # Positive reward for timely collection\n",
+        "            waste_level = 0  # Waste collected\n",
+        "        elif waste_level < threshold and action == 1:\n",
+        "            reward = -5  # Penalty for collecting too early\n",
+        "\n",
+        "        rewards.append(reward)\n",
+        "\n",
+        "        done = time >= 20  # Example end condition (episode ends after 20 timesteps)\n",
+        "\n",
+        "        # Remember the experience\n",
+        "        agent.remember(state, action, reward, next_state, done)\n",
+        "\n",
+        "        # Move to the next state\n",
+        "        state = next_state\n",
+        "        time += 1\n",
+        "\n",
+        "    # Replay experience to train the model\n",
+        "    if len(agent.memory) > 32:\n",
+        "        agent.replay(32)\n",
+        "\n",
+        "    # Store metrics\n",
+        "    episode_rewards.append(np.mean(rewards))\n",
+        "    epsilon_values.append(agent.epsilon)\n",
+        "    overflow_events_per_episode.append(overflow_count)\n",
+        "\n",
+        "    # Print progress\n",
+        "    print(f\"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {agent.epsilon:.2f}, Overflow Events: {overflow_count}, Average Reward: {np.mean(rewards):.2f}\")\n",
+        "\n",
+        "# Visualization\n",
+        "plt.figure(figsize=(15, 5))\n",
+        "\n",
+        "# Plot Average Reward per Episode\n",
+        "plt.subplot(1, 3, 1)\n",
+        "plt.plot(episode_rewards)\n",
+        "plt.xlabel('Episode')\n",
+        "plt.ylabel('Average Reward')\n",
+        "plt.title('Average Reward per Episode')\n",
+        "\n",
+        "# Plot Epsilon Decay\n",
+        "plt.subplot(1, 3, 2)\n",
+        "plt.plot(epsilon_values)\n",
+        "plt.xlabel('Episode')\n",
+        "plt.ylabel('Epsilon')\n",
+        "plt.title('Epsilon Decay')\n",
+        "\n",
+        "# Plot Overflow Events per Episode\n",
+        "plt.subplot(1, 3, 3)\n",
+        "plt.plot(overflow_events_per_episode)\n",
+        "plt.xlabel('Episode')\n",
+        "plt.ylabel('Overflow Events')\n",
+        "plt.title('Overflow Events per Episode')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/Waste Management through Reinforcement Learning techniques/gitignore (3).txt b/Waste Management through Reinforcement Learning techniques/gitignore (3).txt
new file mode 100644
index 0000000000..e20f67d6c7
--- /dev/null
+++ b/Waste Management through Reinforcement Learning techniques/gitignore (3).txt	
@@ -0,0 +1,7 @@
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.h5
+*.weights
+.env
diff --git a/Waste Management through Reinforcement Learning techniques/requirements (3).txt b/Waste Management through Reinforcement Learning techniques/requirements (3).txt
new file mode 100644
index 0000000000..e63a2d2868
--- /dev/null
+++ b/Waste Management through Reinforcement Learning techniques/requirements (3).txt	
@@ -0,0 +1,4 @@
+numpy
+random
+tensorflow
+matplotlib
diff --git a/Waste Management through Reinforcement Learning techniques/untitled58 (1).py b/Waste Management through Reinforcement Learning techniques/untitled58 (1).py
new file mode 100644
index 0000000000..8c820a42bb
--- /dev/null
+++ b/Waste Management through Reinforcement Learning techniques/untitled58 (1).py	
@@ -0,0 +1,304 @@
+# -*- coding: utf-8 -*-
+"""Untitled58.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1UY_NK3eSJ4ybttbkUcNuKIUL0YzPkc68
+"""
+
+import numpy as np
+import random
+from collections import deque
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.optimizers import Adam
+
+class EnergyManagementAgent:
+    def __init__(self, state_size, action_size):
+        self.state_size = state_size
+        self.action_size = action_size
+        self.memory = deque(maxlen=2000)
+        self.gamma = 0.95  # discount rate
+        self.epsilon = 1.0  # exploration rate
+        self.epsilon_min = 0.01
+        self.epsilon_decay = 0.995
+        self.learning_rate = 0.001
+        self.model = self._build_model()
+
+    def _build_model(self):
+        model = Sequential()
+        model.add(tf.keras.Input(shape=(self.state_size,)))
+        model.add(Dense(24, activation='relu'))
+        model.add(Dense(24, activation='relu'))
+        model.add(Dense(self.action_size, activation='linear'))
+        model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate))
+        return model
+
+    def remember(self, state, action, reward, next_state, done):
+        self.memory.append((state, action, reward, next_state, done))
+
+    def act(self, state):
+        if np.random.rand() <= self.epsilon:
+            return random.randrange(self.action_size)
+        act_values = self.model.predict(state, verbose=0)
+        return np.argmax(act_values[0])
+
+    def replay(self, batch_size):
+        minibatch = random.sample(self.memory, batch_size)
+        for state, action, reward, next_state, done in minibatch:
+            target = reward
+            if not done:
+                target = reward + self.gamma * np.amax(self.model.predict(next_state, verbose=0)[0])
+            target_f = self.model.predict(state, verbose=0)
+            target_f[0][action] = target
+            self.model.fit(state, target_f, epochs=1, verbose=0)
+        if self.epsilon > self.epsilon_min:
+            self.epsilon *= self.epsilon_decay
+
+    def load(self, name):
+        self.model.load_weights(name)
+
+    def save(self, name):
+        self.model.save_weights(name)
+
+if __name__ == "__main__":
+    # Environment parameters
+    state_size = 4  # Example state: [current energy usage, time of day, temperature, price]
+    action_size = 2  # Example actions: [0: reduce usage, 1: maintain usage]
+    agent = EnergyManagementAgent(state_size, action_size)
+    episodes = 100
+
+    # Train the agent in the environment
+    for e in range(episodes):
+        # Reset environment for each episode
+        state = np.reshape(np.random.rand(state_size), [1, state_size])
+        done = False
+        time = 0
+
+        while not done:
+            # Take action
+            action = agent.act(state)
+            # Simulate next state and reward (replace with environment logic)
+            next_state = np.reshape(np.random.rand(state_size), [1, state_size])
+            reward = random.uniform(-1, 1)  # Example reward
+            done = time >= 20  # Example end condition
+
+            # Remember the experience
+            agent.remember(state, action, reward, next_state, done)
+
+            # Move to the next state
+            state = next_state
+            time += 1
+
+        # Replay experience to train the model
+        if len(agent.memory) > 32:
+            agent.replay(32)
+
+        # Print progress
+        print(f"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {agent.epsilon:.2f}")
+
+state_size = 4  # Example state: [waste level, time of day, weather, distance to collection point]
+action_size = 2  # Example actions: [0: wait, 1: collect waste]
+
+# Environment parameters
+state_size = 4  # Example state: [waste level, time of day, weather, distance to collection point]
+action_size = 2  # Example actions: [0: wait, 1: collect waste]
+agent = EnergyManagementAgent(state_size, action_size)
+episodes = 100
+
+# Waste management specific parameters
+threshold = 0.7  # Waste level threshold (between 0 and 1, where 1 means full capacity)
+
+# Train the agent in the environment
+for e in range(episodes):
+    # Reset environment for each episode
+    waste_level = random.uniform(0, 0.5)  # Start with a random waste level below the threshold
+    state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size])
+    done = False
+    time = 0
+    overflow_count = 0  # Track overflow events
+    rewards = []
+
+    while not done:
+        # Take action
+        action = agent.act(state)
+        # Simulate next state and reward (replace with environment logic)
+        waste_level += random.uniform(0, 0.1)  # Waste increases over time
+        if waste_level > 1.0:  # Overflow occurred
+            overflow_count += 1
+            waste_level = 1.0
+
+        next_state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size])
+
+        # Reward structure
+        reward = -1  # Default reward for time passing
+        if waste_level > threshold and action == 1:
+            reward = 10  # Positive reward for timely collection
+            waste_level = 0  # Waste collected
+        elif waste_level < threshold and action == 1:
+            reward = -5  # Penalty for collecting too early
+
+        rewards.append(reward)
+
+        done = time >= 20  # Example end condition
+
+        # Remember the experience
+        agent.remember(state, action, reward, next_state, done)
+
+        # Move to the next state
+        state = next_state
+        time += 1
+
+    # Replay experience to train the model
+    if len(agent.memory) > 32:
+        agent.replay(32)
+
+    # Print progress
+    print(f"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {agent.epsilon:.2f}, Overflow Events: {overflow_count}, Average Reward: {np.mean(rewards):.2f}")
+plt.figure(figsize=(15, 5))
+
+import numpy as np
+import random
+from collections import deque
+import matplotlib.pyplot as plt
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.optimizers import Adam
+
+class EnergyManagementAgent:
+    def __init__(self, state_size, action_size):
+        self.state_size = state_size
+        self.action_size = action_size
+        self.memory = deque(maxlen=2000)
+        self.gamma = 0.95    # discount rate
+        self.epsilon = 1.0   # exploration rate
+        self.epsilon_min = 0.01
+        self.epsilon_decay = 0.995
+        self.learning_rate = 0.001
+        self.model = self._build_model()
+
+    def _build_model(self):
+        model = Sequential()
+        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
+        model.add(Dense(24, activation='relu'))
+        model.add(Dense(self.action_size, activation='linear'))
+        # Update learning rate parameter
+        model.compile(loss='mse', optimizer=Adam(learning_rate=self.learning_rate))
+        return model
+
+    def remember(self, state, action, reward, next_state, done):
+        self.memory.append((state, action, reward, next_state, done))
+
+    def act(self, state):
+        if np.random.rand() <= self.epsilon:
+            return random.randrange(self.action_size)
+        act_values = self.model.predict(state)
+        return np.argmax(act_values[0])
+
+    def replay(self, batch_size):
+        minibatch = random.sample(self.memory, batch_size)
+        for state, action, reward, next_state, done in minibatch:
+            target = reward
+            if not done:
+                target = (reward + self.gamma *
+                          np.amax(self.model.predict(next_state)[0]))
+            target_f = self.model.predict(state)
+            target_f[0][action] = target
+            self.model.fit(state, target_f, epochs=1, verbose=0)
+        if self.epsilon > self.epsilon_min:
+            self.epsilon *= self.epsilon_decay
+
+# Environment parameters
+state_size = 4  # Example state: [waste level, time of day, weather, distance to collection point]
+action_size = 2  # Example actions: [0: wait, 1: collect waste]
+agent = EnergyManagementAgent(state_size, action_size)
+episodes = 100
+
+# Waste management specific parameters
+threshold = 0.7  # Waste level threshold (between 0 and 1, where 1 means full capacity)
+
+# Tracking metrics
+episode_rewards = []
+epsilon_values = []
+overflow_events_per_episode = []
+
+# Train the agent in the environment
+for e in range(episodes):
+    # Reset environment for each episode
+    waste_level = random.uniform(0, 0.5)  # Start with a random waste level below the threshold
+    state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size])
+    done = False
+    time = 0
+    overflow_count = 0  # Track overflow events
+    rewards = []
+
+    while not done:
+        # Take action
+        action = agent.act(state)
+        # Simulate next state and reward (replace with environment logic)
+        waste_level += random.uniform(0, 0.1)  # Waste increases over time
+        if waste_level > 1.0:  # Overflow occurred
+            overflow_count += 1
+            waste_level = 1.0
+
+        next_state = np.reshape([waste_level, random.uniform(0, 24), random.uniform(0, 1), random.uniform(0, 10)], [1, state_size])
+
+        # Reward structure
+        reward = -1  # Default reward for time passing
+        if waste_level > threshold and action == 1:
+            reward = 10  # Positive reward for timely collection
+            waste_level = 0  # Waste collected
+        elif waste_level < threshold and action == 1:
+            reward = -5  # Penalty for collecting too early
+
+        rewards.append(reward)
+
+        done = time >= 20  # Example end condition (episode ends after 20 timesteps)
+
+        # Remember the experience
+        agent.remember(state, action, reward, next_state, done)
+
+        # Move to the next state
+        state = next_state
+        time += 1
+
+    # Replay experience to train the model
+    if len(agent.memory) > 32:
+        agent.replay(32)
+
+    # Store metrics
+    episode_rewards.append(np.mean(rewards))
+    epsilon_values.append(agent.epsilon)
+    overflow_events_per_episode.append(overflow_count)
+
+    # Print progress
+    print(f"Episode: {e+1}/{episodes}, Score: {time}, Epsilon: {agent.epsilon:.2f}, Overflow Events: {overflow_count}, Average Reward: {np.mean(rewards):.2f}")
+
+# Visualization
+plt.figure(figsize=(15, 5))
+
+# Plot Average Reward per Episode
+plt.subplot(1, 3, 1)
+plt.plot(episode_rewards)
+plt.xlabel('Episode')
+plt.ylabel('Average Reward')
+plt.title('Average Reward per Episode')
+
+# Plot Epsilon Decay
+plt.subplot(1, 3, 2)
+plt.plot(epsilon_values)
+plt.xlabel('Episode')
+plt.ylabel('Epsilon')
+plt.title('Epsilon Decay')
+
+# Plot Overflow Events per Episode
+plt.subplot(1, 3, 3)
+plt.plot(overflow_events_per_episode)
+plt.xlabel('Episode')
+plt.ylabel('Overflow Events')
+plt.title('Overflow Events per Episode')
+
+plt.tight_layout()
+plt.show()
\ No newline at end of file

From 626b5917f741627f46a30c75c34a6dac7ebc1394 Mon Sep 17 00:00:00 2001
From: Panchadip <165953910+Panchadip-128@users.noreply.github.com>
Date: Fri, 25 Oct 2024 01:29:18 +0530
Subject: [PATCH 5/8] Rename README (15).md to README.md

---
 .../{README (15).md => README.md}                                 | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Waste Management through Reinforcement Learning techniques/{README (15).md => README.md} (100%)

diff --git a/Waste Management through Reinforcement Learning techniques/README (15).md b/Waste Management through Reinforcement Learning techniques/README.md
similarity index 100%
rename from Waste Management through Reinforcement Learning techniques/README (15).md
rename to Waste Management through Reinforcement Learning techniques/README.md

From 09cbfe08de91b0e0cb382742ef29a42be0bb1142 Mon Sep 17 00:00:00 2001
From: Panchadip <165953910+Panchadip-128@users.noreply.github.com>
Date: Fri, 25 Oct 2024 01:29:50 +0530
Subject: [PATCH 6/8] Rename
 Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning (1).ipynb to
 Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning.ipynb

---
 ...ng_Epsilon_Decay_and_Scores_in_Reinforcement_Learning.ipynb} | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)
 rename Waste Management through Reinforcement Learning techniques/{Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning (1).ipynb => Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning.ipynb} (99%)

diff --git a/Waste Management through Reinforcement Learning techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning (1).ipynb b/Waste Management through Reinforcement Learning techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning.ipynb
similarity index 99%
rename from Waste Management through Reinforcement Learning techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning (1).ipynb
rename to Waste Management through Reinforcement Learning techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning.ipynb
index e81023f810..2f93c32b89 100644
--- a/Waste Management through Reinforcement Learning techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning (1).ipynb	
+++ b/Waste Management through Reinforcement Learning techniques/Visualizing_Epsilon_Decay_and_Scores_in_Reinforcement_Learning.ipynb	
@@ -330,4 +330,4 @@
       ]
     }
   ]
-}
\ No newline at end of file
+}

From 79d8d27455592840fce791481ae71e07c5aa8570 Mon Sep 17 00:00:00 2001
From: Panchadip <165953910+Panchadip-128@users.noreply.github.com>
Date: Fri, 25 Oct 2024 01:30:10 +0530
Subject: [PATCH 7/8] Rename gitignore (3).txt to gitignore.txt

---
 .../{gitignore (3).txt => gitignore.txt}                          | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Waste Management through Reinforcement Learning techniques/{gitignore (3).txt => gitignore.txt} (100%)

diff --git a/Waste Management through Reinforcement Learning techniques/gitignore (3).txt b/Waste Management through Reinforcement Learning techniques/gitignore.txt
similarity index 100%
rename from Waste Management through Reinforcement Learning techniques/gitignore (3).txt
rename to Waste Management through Reinforcement Learning techniques/gitignore.txt

From 8d846bb5593478d0131b35836ba707b250c4d6fd Mon Sep 17 00:00:00 2001
From: Panchadip <165953910+Panchadip-128@users.noreply.github.com>
Date: Fri, 25 Oct 2024 01:30:37 +0530
Subject: [PATCH 8/8] Rename requirements (3).txt to requirements.txt

---
 .../{requirements (3).txt => requirements.txt}                    | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Waste Management through Reinforcement Learning techniques/{requirements (3).txt => requirements.txt} (100%)

diff --git a/Waste Management through Reinforcement Learning techniques/requirements (3).txt b/Waste Management through Reinforcement Learning techniques/requirements.txt
similarity index 100%
rename from Waste Management through Reinforcement Learning techniques/requirements (3).txt
rename to Waste Management through Reinforcement Learning techniques/requirements.txt