Notebook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BrouthenKamel/GDG-QAADA/blob/main/QAADA_GDG_Discover_Reinforcement_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7sUukH74yQ7K"
      },
      "source": [
        "### Step 0: Setup a Virtual Display 💻\n",
        "\n",
        "During the notebook, we'll need to generate a replay video. To do so, with colab, **we need to have a virtual screen to be able to render the environment** (and thus record the frames). \n",
        "\n",
        "Hence the following cell will install virtual screen libraries and create and run a virtual screen 🖥"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "f2wrVUvrySkW"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install pyglet==1.5.1 \n",
        "!apt install python-opengl\n",
        "!apt install ffmpeg\n",
        "!apt install xvfb\n",
        "!pip3 install pyvirtualdisplay\n",
        "\n",
        "# Virtual display\n",
        "from pyvirtualdisplay import Display\n",
        "\n",
        "virtual_display = Display(visible=0, size=(1400, 900))\n",
        "virtual_display.start()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mIkOB4Rpw65k"
      },
      "source": [
        "### Step 1: Install dependencies 🔽\n",
        "The first step is to install the dependencies, we’ll install multiple ones:\n",
        "\n",
        "- `gym`: Contains the FrozenLake-v1 ⛄ and Taxi-v3 🚕 environments.\n",
        "- `pygame`: Used for the FrozenLake-v1 and Taxi-v3 UI.\n",
        "- `numPy`: Used for handling our Q-table.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "1Ac7wW_5ClJC"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install gym==0.24 # We install the newest gym version for the Taxi-v3 \"rgb_array version\"\n",
        "!pip install pygame\n",
        "!pip install numpy\n",
        "\n",
        "!pip install huggingface_hub\n",
        "!pip install pickle5\n",
        "!pip install pyyaml==6.0 # avoid key error metadata\n",
        "!pip install imageio imageio_ffmpeg"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W-7f-Swax_9x"
      },
      "source": [
        "### Step 2: Import the packages 📦\n",
        "\n",
        "In addition to the installed libraries, we also use:\n",
        "\n",
        "- `random`: To generate random numbers (that will be useful for Epsilon-Greedy Policy).\n",
        "- `imageio`: To generate a replay video\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "VcNvOAQlysBJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6fe31b4-2dfb-4b4a-cf7c-634bbd5b35e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: Gym version v0.24.0 has a number of critical issues with `gym.make` such that the `reset` and `step` functions are called before returning the environment. It is recommend to downgrading to v0.23.1 or upgrading to v0.25.1\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import gym\n",
        "import random\n",
        "import imageio\n",
        "import os\n",
        "\n",
        "import pickle5 as pickle\n",
        "from tqdm.notebook import tqdm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xp4-bXKIy1mQ"
      },
      "source": [
        "We're now ready to code our Q-Learning algorithm 🔥"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xya49aNJWVvv"
      },
      "source": [
        "**Frozen Lake ⛄**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NAvihuHdy9tw"
      },
      "source": [
        "### Step 1: Create and understand [FrozenLake environment ⛄]((https://www.gymlibrary.dev/environments/toy_text/frozen_lake/)\n",
        "---\n",
        "\n",
        "💡 A good habit when you start to use an environment is to check its documentation \n",
        "\n",
        "👉 https://www.gymlibrary.dev/environments/toy_text/frozen_lake/\n",
        "\n",
        "---\n",
        "\n",
        "We're going to train our Q-Learning agent **to navigate from the starting state (S) to the goal state (G) by walking only on frozen tiles (F) and avoid holes (H)**.\n",
        "\n",
        "We can have two sizes of environment:\n",
        "- `map_name=\"4x4\"`: a 4x4 grid version\n",
        "- `map_name=\"8x8\"`: a 8x8 grid version\n",
        "\n",
        "\n",
        "The environment has two modes:\n",
        "- `is_slippery=False`: The agent always move in the intended direction due to the non-slippery nature of the frozen lake.\n",
        "- `is_slippery=True`: The agent may not always move in the intended direction due to the slippery nature of the frozen lake (stochastic)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UaW_LHfS0PY2"
      },
      "source": [
        "For now let's keep it simple with the 4x4 map and non-slippery"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "jNxUbPMP0akP"
      },
      "outputs": [],
      "source": [
        "env = gym.make(\"FrozenLake-v1\", map_name=\"4x4\", is_slippery=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXbTfdeJ1Xi9"
      },
      "source": [
        "### Let's see what the Environment looks like:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "ZNPG0g_UGCfh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7a222ec-6da2-4383-8d7c-732075e07b91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "_____OBSERVATION SPACE_____ \n",
            "\n",
            "Observation Space Discrete(16)\n",
            "Sample observation 10\n"
          ]
        }
      ],
      "source": [
        "# We create our environment with gym.make(\"<name_of_the_environment>\")\n",
        "env.reset()\n",
        "print(\"_____OBSERVATION SPACE_____ \\n\")\n",
        "print(\"Observation Space\", env.observation_space)\n",
        "print(\"Sample observation\", env.observation_space.sample()) # Get a random observation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2MXc15qFE0M9"
      },
      "source": [
        "We see with `Observation Space Shape Discrete(16)` that the observation is a value representing the **agent’s current position as current_row * nrows + current_col (where both the row and col start at 0)**. \n",
        "\n",
        "For example, the goal position in the 4x4 map can be calculated as follows: 3 * 4 + 3 = 15. The number of possible observations is dependent on the size of the map. **For example, the 4x4 map has 16 possible observations.**\n",
        "\n",
        "\n",
        "For instance, this is what state = 0 looks like:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nW-hW7n22PI_"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "We5WqOBGLoSm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebc9d21f-d3a0-49ea-f8b5-a5deabb2c0b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " _____ACTION SPACE_____ \n",
            "\n",
            "Action Space Shape 4\n",
            "Action Space Sample 0\n"
          ]
        }
      ],
      "source": [
        "print(\"\\n _____ACTION SPACE_____ \\n\")\n",
        "print(\"Action Space Shape\", env.action_space.n)\n",
        "print(\"Action Space Sample\", env.action_space.sample()) # Take a random action"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MyxXwkI2Magx"
      },
      "source": [
        "The action space (the set of possible actions the agent can take) is discrete with 4 actions available 🎮:\n",
        "- 0: GO LEFT\n",
        "- 1: GO DOWN\n",
        "- 2: GO RIGHT\n",
        "- 3: GO UP\n",
        "\n",
        "Reward function 💰:\n",
        "- Reach goal: +1\n",
        "- Reach hole: 0\n",
        "- Reach frozen: 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "y3ZCdluj3k0l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec3ba48d-9120-4992-851d-1420763bfaa1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are  16  possible states\n",
            "There are  4  possible actions\n"
          ]
        }
      ],
      "source": [
        "state_space = 16\n",
        "print(\"There are \", state_space, \" possible states\")\n",
        "\n",
        "action_space = 4\n",
        "print(\"There are \", action_space, \" possible actions\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "rCddoOXM3UQH"
      },
      "outputs": [],
      "source": [
        "# Let's create our Qtable of size (state_space, action_space) and initialized each values at 0 using np.zeros\n",
        "def initialize_q_table(state_space, action_space):\n",
        "  Qtable = np.zeros((state_space, action_space))\n",
        "  return Qtable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "9YfvrqRt3jdR"
      },
      "outputs": [],
      "source": [
        "Qtable_frozenlake = initialize_q_table(state_space, action_space)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flILKhBU3yZ7"
      },
      "source": [
        "### Step 3: Define the epsilon-greedy policy 🤖\n",
        "\n",
        "Epsilon-Greedy is the training policy that handles the exploration/exploitation trade-off.\n",
        "\n",
        "The idea with Epsilon Greedy:\n",
        "\n",
        "- With *probability 1 - ɛ* : **we do exploitation** (aka our agent selects the action with the highest state-action pair value).\n",
        "\n",
        "- With *probability ɛ*: we do **exploration** (trying random action).\n",
        "\n",
        "And as the training goes, we progressively **reduce the epsilon value since we will need less and less exploration and more exploitation.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "cYxHuckr4LiG"
      },
      "outputs": [],
      "source": [
        "def epsilon_greedy_policy(Qtable, state, epsilon):\n",
        "  # Randomly generate a number between 0 and 1\n",
        "  random_int = random.uniform(0,1)\n",
        "  # if random_int > greater than epsilon --> exploitation\n",
        "  if random_int > epsilon:\n",
        "    # Take the action with the highest value given a state\n",
        "    # np.argmax can be useful here\n",
        "    action = np.argmax(Qtable[state])\n",
        "  # else --> exploration\n",
        "  else:\n",
        "    action = env.action_space.sample()\n",
        "  \n",
        "  return action"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Atll4Z774gri"
      },
      "source": [
        "### Step 4: Define the greedy policy 🤖\n",
        "Remember we have two policies since Q-Learning is an **off-policy** algorithm. This means we're using a **different policy for acting and updating the value function**.\n",
        "\n",
        "- Epsilon greedy policy (acting policy)\n",
        "- Greedy policy (updating policy)\n",
        "\n",
        "Greedy policy will also be the final policy we'll have when the Q-learning agent will be trained. The greedy policy is used to select an action from the Q-table."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "E3SCLmLX5bWG"
      },
      "outputs": [],
      "source": [
        "def greedy_policy(Qtable, state):\n",
        "  # Exploitation: take the action with the highest state, action value\n",
        "  action = np.argmax(Qtable[state])\n",
        "  \n",
        "  return action"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hW80DealcRtu"
      },
      "source": [
        "### Step 5: Define the hyperparameters ⚙️\n",
        "The exploration related hyperparamters are some of the most important ones. \n",
        "\n",
        "- We need to make sure that our agent **explores enough the state space** in order to learn a good value approximation, in order to do that we need to have progressive decay of the epsilon.\n",
        "- If you decrease too fast epsilon (too high decay_rate), **you take the risk that your agent is stuck**, since your agent didn't explore enough the state space and hence can't solve the problem."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Y1tWn0tycWZ1"
      },
      "outputs": [],
      "source": [
        "# Training parameters\n",
        "n_training_episodes = 10000  # Total training episodes\n",
        "learning_rate = 0.7          # Learning rate\n",
        "\n",
        "# Evaluation parameters\n",
        "n_eval_episodes = 100        # Total number of test episodes\n",
        "\n",
        "# Environment parameters\n",
        "env_id = \"FrozenLake-v1\"     # Name of the environment\n",
        "max_steps = 99               # Max steps per episode\n",
        "gamma = 0.95                 # Discounting rate\n",
        "eval_seed = []               # The evaluation seed of the environment\n",
        "\n",
        "# Exploration parameters\n",
        "max_epsilon = 1.0             # Exploration probability at start\n",
        "min_epsilon = 0.05            # Minimum exploration probability \n",
        "decay_rate = 0.0005            # Exponential decay rate for exploration prob"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cDb7Tdx8atfL"
      },
      "source": [
        "### Step 6: Create the training loop method"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "IyZaYbUAeolw"
      },
      "outputs": [],
      "source": [
        "def train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable):\n",
        "  for episode in tqdm(range(n_training_episodes)):\n",
        "    # Reduce epsilon (because we need less and less exploration)\n",
        "    epsilon = min_epsilon + (max_epsilon - min_epsilon)*np.exp(-decay_rate*episode)\n",
        "    # Reset the environment\n",
        "    state = env.reset()\n",
        "    step = 0\n",
        "    done = False\n",
        "\n",
        "    # repeat\n",
        "    for step in range(max_steps):\n",
        "      # Choose the action At using epsilon greedy policy\n",
        "      action = epsilon_greedy_policy(Qtable, state, epsilon)\n",
        "\n",
        "      # Take action At and observe Rt+1 and St+1\n",
        "      # Take the action (a) and observe the outcome state(s') and reward (r)\n",
        "      new_state, reward, done, info = env.step(action)\n",
        "\n",
        "      # Update Q(s,a):= Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]\n",
        "      Qtable[state][action] = Qtable[state][action] + learning_rate * (reward + gamma * np.max(Qtable[new_state]) - Qtable[state][action])   \n",
        "\n",
        "      # If done, finish the episode\n",
        "      if done:\n",
        "        break\n",
        "      \n",
        "      # Our state is the new state\n",
        "      state = new_state\n",
        "  return Qtable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLwKQ4tUdhGI"
      },
      "source": [
        "### Step 7: Train the Q-Learning agent 🏃"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "DPBxfjJdTCOH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "2b8ffd2e89ba4a73b3d6da521cff433f",
            "924b59f893d24684bc104bb47de0d1eb",
            "5c74ef3246e54e328f17eddf8a4635e8",
            "8886a490b27c4652891c078347395c12",
            "8b2dc208a2ff4b019bcda844bf87d502",
            "afa599d007cb4e719df2daad86638b69",
            "5abc08fa02c24ac3ba890200361e0ff6",
            "4fac1dd9e8514d6a85f0f6ebf2d1e627",
            "82d5d73c1cb940c7bb11da9bb6046133",
            "72468a6ad7834809a14ef9b433101838",
            "714dcab9224545808c83175855d2d69f"
          ]
        },
        "outputId": "4ff22f45-e504-4e7a-86a4-8119a945cca1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b8ffd2e89ba4a73b3d6da521cff433f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "Qtable_frozenlake = train(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable_frozenlake)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yVeEhUCrc30L"
      },
      "source": [
        "### Step 8: Let's see what our Q-Learning table looks like now 👀"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "nmfchsTITw4q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b83afe7f-aae9-4368-e1b0-97e7610011e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.73509189, 0.77378094, 0.77378094, 0.73509189],\n",
              "       [0.73509189, 0.        , 0.81450625, 0.77378094],\n",
              "       [0.77378094, 0.857375  , 0.77378094, 0.81450625],\n",
              "       [0.81450625, 0.        , 0.77378094, 0.77378094],\n",
              "       [0.77378094, 0.81450625, 0.        , 0.73509189],\n",
              "       [0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.9025    , 0.        , 0.81450625],\n",
              "       [0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.81450625, 0.        , 0.857375  , 0.77378094],\n",
              "       [0.81450625, 0.9025    , 0.9025    , 0.        ],\n",
              "       [0.857375  , 0.95      , 0.        , 0.857375  ],\n",
              "       [0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.9025    , 0.95      , 0.857375  ],\n",
              "       [0.9025    , 0.95      , 1.        , 0.9025    ],\n",
              "       [0.        , 0.        , 0.        , 0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "Qtable_frozenlake"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUrWkxsHccXD"
      },
      "source": [
        "### Step 9: Define the evaluation method 📝"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "jNl0_JO2cbkm"
      },
      "outputs": [],
      "source": [
        "def evaluate_agent(env, max_steps, n_eval_episodes, Q, seed):\n",
        "  \"\"\"\n",
        "  Evaluate the agent for ``n_eval_episodes`` episodes and returns average reward and std of reward.\n",
        "  :param env: The evaluation environment\n",
        "  :param n_eval_episodes: Number of episode to evaluate the agent\n",
        "  :param Q: The Q-table\n",
        "  :param seed: The evaluation seed array (for taxi-v3)\n",
        "  \"\"\"\n",
        "  episode_rewards = []\n",
        "  for episode in tqdm(range(n_eval_episodes)):\n",
        "    if seed:\n",
        "      state = env.reset(seed=seed[episode])\n",
        "    else:\n",
        "      state = env.reset()\n",
        "    step = 0\n",
        "    done = False\n",
        "    total_rewards_ep = 0\n",
        "    \n",
        "    for step in range(max_steps):\n",
        "      # Take the action (index) that have the maximum expected future reward given that state\n",
        "      action = np.argmax(Q[state][:])\n",
        "      new_state, reward, done, info = env.step(action)\n",
        "      total_rewards_ep += reward\n",
        "        \n",
        "      if done:\n",
        "        break\n",
        "      state = new_state\n",
        "    episode_rewards.append(total_rewards_ep)\n",
        "  mean_reward = np.mean(episode_rewards)\n",
        "  std_reward = np.std(episode_rewards)\n",
        "\n",
        "  return mean_reward, std_reward"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0jJqjaoAnxUo"
      },
      "source": [
        "### Step 10: Evaluate our Q-Learning agent 📈\n",
        "- Normally you should have mean reward of 1.0\n",
        "- It's relatively easy since the state space is really small (16). What you can try to do is [to replace with the slippery version](https://www.gymlibrary.dev/environments/toy_text/frozen_lake/)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "fAgB7s0HEFMm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "3756f7577b0c4300825b79b2dde006a3",
            "825e1458344542e9aa6fbb41d07fae89",
            "b9a3d1c6798a4b30bb9cf0e16ce2a47d",
            "3f03b610e3aa41e09c759f9b72c376c2",
            "54edea2922424ff49b1a492455f0b308",
            "940eed55f64440f0ab857c22e4a23dab",
            "89bd1639da9645d4b299236eb5f41e04",
            "55285e9f68a746509a7f53013f5f9ea6",
            "c564c920f630429db9747d4807e6b3af",
            "c273df9ebc2d42b9a731b7ba8262f6e1",
            "5bc0e44bbd864c3f963257d183794ff0"
          ]
        },
        "outputId": "e498bf51-dd0a-4c32-aba1-a993912c39fa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3756f7577b0c4300825b79b2dde006a3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean_reward=1.00 +/- 0.00\n"
          ]
        }
      ],
      "source": [
        "# Evaluate our Agent\n",
        "mean_reward, std_reward = evaluate_agent(env, max_steps, n_eval_episodes, Qtable_frozenlake, eval_seed)\n",
        "print(f\"Mean_reward={mean_reward:.2f} +/- {std_reward:.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yxaP3bPdg1DV"
      },
      "source": [
        "### Step 11: Publish our trained model on the Hub 🔥\n",
        "Now that we saw we got good results after the training, we can publish our trained model on the hub 🤗 with one line of code.\n",
        "\n",
        "Here's an example of a Model Card:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZ5LrR-joIHD"
      },
      "source": [
        "#### Do not modify this code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Jex3i9lZ8ksX"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "from huggingface_hub import HfApi, HfFolder, Repository\n",
        "from huggingface_hub.repocard import metadata_eval_result, metadata_save\n",
        "\n",
        "from pathlib import Path\n",
        "import datetime\n",
        "import json"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "Qo57HBn3W74O"
      },
      "outputs": [],
      "source": [
        "def record_video(env, Qtable, out_directory, fps=1):\n",
        "  images = []  \n",
        "  done = False\n",
        "  state = env.reset(seed=random.randint(0,500))\n",
        "  img = env.render(mode='rgb_array')\n",
        "  images.append(img)\n",
        "  while not done:\n",
        "    # Take the action (index) that have the maximum expected future reward given that state\n",
        "    action = np.argmax(Qtable[state][:])\n",
        "    state, reward, done, info = env.step(action) # We directly put next_state = state for recording logic\n",
        "    img = env.render(mode='rgb_array')\n",
        "    images.append(img)\n",
        "  imageio.mimsave(out_directory, [np.array(img) for i, img in enumerate(images)], fps=fps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "pwsNrzB339aF"
      },
      "outputs": [],
      "source": [
        "def push_to_hub(repo_id, \n",
        "                model,\n",
        "                env,\n",
        "                video_fps=1,\n",
        "                local_repo_path=\"hub\",\n",
        "                commit_message=\"Push Q-Learning agent to Hub\",\n",
        "                token= None\n",
        "                ):\n",
        "  _, repo_name = repo_id.split(\"/\")\n",
        "\n",
        "  eval_env = env\n",
        "  \n",
        "  # Step 1: Clone or create the repo\n",
        "  # Create the repo (or clone its content if it's nonempty)\n",
        "  api = HfApi()\n",
        "  \n",
        "  repo_url = api.create_repo(\n",
        "        repo_id=repo_id,\n",
        "        token=token,\n",
        "        private=False,\n",
        "        exist_ok=True,)\n",
        "  \n",
        "  # Git pull\n",
        "  repo_local_path = Path(local_repo_path) / repo_name\n",
        "  repo = Repository(repo_local_path, clone_from=repo_url, use_auth_token=True)\n",
        "  repo.git_pull()\n",
        "  \n",
        "  repo.lfs_track([\"*.mp4\"])\n",
        "\n",
        "  # Step 1: Save the model\n",
        "  if env.spec.kwargs.get(\"map_name\"):\n",
        "    model[\"map_name\"] = env.spec.kwargs.get(\"map_name\")\n",
        "    if env.spec.kwargs.get(\"is_slippery\", \"\") == False:\n",
        "      model[\"slippery\"] = False\n",
        "\n",
        "  print(model)\n",
        "  \n",
        "    \n",
        "  # Pickle the model\n",
        "  with open(Path(repo_local_path)/'q-learning.pkl', 'wb') as f:\n",
        "    pickle.dump(model, f)\n",
        "  \n",
        "  # Step 2: Evaluate the model and build JSON\n",
        "  mean_reward, std_reward = evaluate_agent(eval_env, model[\"max_steps\"], model[\"n_eval_episodes\"], model[\"qtable\"], model[\"eval_seed\"])\n",
        "\n",
        "  # First get datetime\n",
        "  eval_datetime = datetime.datetime.now()\n",
        "  eval_form_datetime = eval_datetime.isoformat()\n",
        "\n",
        "  evaluate_data = {\n",
        "        \"env_id\": model[\"env_id\"], \n",
        "        \"mean_reward\": mean_reward,\n",
        "        \"n_eval_episodes\": model[\"n_eval_episodes\"],\n",
        "        \"eval_datetime\": eval_form_datetime,\n",
        "  }\n",
        "  # Write a JSON file\n",
        "  with open(Path(repo_local_path) / \"results.json\", \"w\") as outfile:\n",
        "      json.dump(evaluate_data, outfile)\n",
        "\n",
        "  # Step 3: Create the model card\n",
        "  # Env id\n",
        "  env_name = model[\"env_id\"]\n",
        "  if env.spec.kwargs.get(\"map_name\"):\n",
        "    env_name += \"-\" + env.spec.kwargs.get(\"map_name\")\n",
        "\n",
        "  if env.spec.kwargs.get(\"is_slippery\", \"\") == False:\n",
        "    env_name += \"-\" + \"no_slippery\"\n",
        "\n",
        "  metadata = {}\n",
        "  metadata[\"tags\"] = [\n",
        "        env_name,\n",
        "        \"q-learning\",\n",
        "        \"reinforcement-learning\",\n",
        "        \"custom-implementation\"\n",
        "    ]\n",
        "\n",
        "  # Add metrics\n",
        "  eval = metadata_eval_result(\n",
        "      model_pretty_name=repo_name,\n",
        "      task_pretty_name=\"reinforcement-learning\",\n",
        "      task_id=\"reinforcement-learning\",\n",
        "      metrics_pretty_name=\"mean_reward\",\n",
        "      metrics_id=\"mean_reward\",\n",
        "      metrics_value=f\"{mean_reward:.2f} +/- {std_reward:.2f}\",\n",
        "      dataset_pretty_name=env_name,\n",
        "      dataset_id=env_name,\n",
        "    )\n",
        "\n",
        "  # Merges both dictionaries\n",
        "  metadata = {**metadata, **eval}\n",
        "\n",
        "  model_card = f\"\"\"\n",
        "  # **Q-Learning** Agent playing **{env_id}**\n",
        "  This is a trained model of a **Q-Learning** agent playing **{env_id}** .\n",
        "  \"\"\"\n",
        "\n",
        "  model_card += \"\"\"\n",
        "  ## Usage\n",
        "  ```python\n",
        "  \"\"\"\n",
        "\n",
        "  model_card += f\"\"\"model = load_from_hub(repo_id=\"{repo_id}\", filename=\"q-learning.pkl\")\n",
        "\n",
        "  # Don't forget to check if you need to add additional attributes (is_slippery=False etc)\n",
        "  env = gym.make(model[\"env_id\"])\n",
        "\n",
        "  evaluate_agent(env, model[\"max_steps\"], model[\"n_eval_episodes\"], model[\"qtable\"], model[\"eval_seed\"])\n",
        "  \"\"\"\n",
        "\n",
        "  model_card +=\"\"\"\n",
        "  ```\n",
        "  \"\"\"\n",
        "\n",
        "  readme_path = repo_local_path / \"README.md\"\n",
        "  readme = \"\"\n",
        "  if readme_path.exists():\n",
        "      with readme_path.open(\"r\", encoding=\"utf8\") as f:\n",
        "        readme = f.read()\n",
        "  else:\n",
        "    readme = model_card\n",
        "\n",
        "  with readme_path.open(\"w\", encoding=\"utf-8\") as f:\n",
        "    f.write(readme)\n",
        "\n",
        "  # Save our metrics to Readme metadata\n",
        "  metadata_save(readme_path, metadata)\n",
        "\n",
        "  # Step 4: Record a video\n",
        "  video_path =  repo_local_path / \"replay.mp4\"\n",
        "  record_video(env, model[\"qtable\"], video_path, video_fps)\n",
        "  \n",
        "  # Push everything to hub\n",
        "  print(f\"Pushing repo {repo_name} to the Hugging Face Hub\")\n",
        "  repo.push_to_hub(commit_message=commit_message)\n",
        "\n",
        "  print(f\"Your model is pushed to the hub. You can view your model here: {repo_url}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cWnFC0iZooTw"
      },
      "source": [
        "To be able to share your model with the community there are three more steps to follow:\n",
        "\n",
        "1️⃣ (If it's not already done) create an account to HF ➡ https://huggingface.co/join\n",
        "\n",
        "2️⃣ Sign in and then, you need to store your authentication token from the Hugging Face website.\n",
        "- Create a new token (https://huggingface.co/settings/tokens) **with write role**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hUExHU_5oqPc"
      },
      "source": [
        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AEyZMgVN05gzxxE5zZ49hwkTJrhFRs7dQ38PiqqqamVx+6uqttZYb9/X7ti5S1144YXq6quvVkeOHlNVXuurqmtsVbVavmKlAtR5XbqoJUuXurYpPl2i7ho/XmVlZaut27a59p8+Y4bq3qOHytu0WVVVW9WRo8fUseMnatK1qjVrv1ft27dX99//gCotK1dV1VY1b958Bah58+a7tmto+t5/zvyqA+TnXO60paVl6t5771PJycnqjVmzlMlkVpaqajVjxgwFqBdeeEFVV1vVofx8dfXVV6uRI0eq/PzDqtpqVSaTWT3+xBPq8ssvVwcPHVJ79+5VF110kbrjjjtUUXGxOnWqUI0ZM0bdcsstKis7W/3000+q2mpVL730kmrfvr1av2GDqrbaVLXV6mFPFRaq/MNHXP6ePXvU4MFD1MiRI1XByVOq2mpVs2b9WwHq/gceUKdLSlS11abMFosyW6rU6ZISNX78eJWcnKzmv/2Oo75Wm6o0mVRVtVWdLilV48ePV1nZ2Wrbtu2OvKutasaMV1WPHj3Ups2b1YkTBWrYsOFqwoQJ6nRJqbJabcpktiiTyayqrTb10087VFZ2tnr66adVVVW1slptqtJkVmazRVmtNp+/EwWO9IYNG65OnChQVptj+dJly1RycrK68847VVFRsbLabOrw4SNq5MiRqkuXLiovL09ZrTa1cqXjPMzNzVUlJaXqgQceUF26dFH//e47R1o2m6tN5s2fr6qtVmW12dTGjRtVjx491Guvv66sNpv6accOlZ2drQYPGaI2bNigrDabKiwqUrfddptKTk5Wa9asUVabTdlsdle67r7Nx6/f2gL4tdYewA/izx7Aj4S1+/r2CFp7AD+4PxXAj5BV7lbV66u6rArgB7CRYsqUKQpQU6ZMUXPmzFGAmjNnToRzcWDweTbppcwqQESklMJms2G1WtHp9S6FxGs71x1djf8///M/XHTRxa5lx44dY/u2beTkDCE1NZVTp04BkNElg1/27OHAgf1kZmbSunVr7HY7R48eIS8vj7Vr16KAffv2Ul5ejtFo9LybrHnWe9wr/cLCUyhVm/7BmvS9x6TatGmN3Wbn6NGj5OVtZO3atVCTX0V5OXFxxto7EFWbc9eu53PJJZegNxgARWa/LACsVhsKxcGDh9iwYQOTH38CQ4yBU6cKAUW3bt149513OHbsOH369CYzsx/bt2+jqKgIs9nM4cOHue9//5cdO3Zy8FA+HTt1ZsfOnWRlZdG583k+7Q2KlJRUUpJTOHWqkM2bNrFhwwZKS0sxm82cLDhBXFwcmzZtIjk5mVvH3UpCQiIohU6nRwOOHTvGtm3buPjii/n1Ndc47gqVwmCIQQOOHzvKtm3byBmS42jbU6dAg4yMDPbs2cOB/Qc4//zzad++HYsXL6ZVqzTuv/9/6dIlA51BD0qRnJJMm9atef3110lOTub2O+4gvW06ms57NM39fKw9zs56r127lrKyMq759TCSU5JRSpHeLp2rrrqa//znP2zduo2s7GxXipaqKl59dQavv/468+bN47JLL0MphcVsYdPmTVx44UD69u1LUWERCkVSUjKdO3dm988/Y7FYXEFD/+xsevfpAwpSU1Lp1asXCxYscMxeVfjUofHHoAjgexTC08drnRvhjEF5W3/51N7Be/uhWQL4DQ+RArWjf+sck/IYg/L23a3ytrh8j/b36YcDWAL4AWz4+EZSs2fPZvx4R+TkPiYF4b8HFfQjPvcnFmlpabRr146jR49SWHiKtLQ0twycoZz7lyWgTes2GI1Gh68UhYWF7Nmzh02bNjFr1iyf/MwmM5oGW7ZsZeqUZ/nyyy/p378/3bp393gGrLmejeN65q0Bp+pJ32Qy1z7zdHu8u2XLFp/8ahtBq93Y5eO5rLYkHv6hgwcpKytj6pRnmTrlWY+1ycnJWK3VxMcn0KtXT/7971kcOnSI08WniYuLY/CvfkWvXj35YcMGLrigD3t/+YX+AwaQmpLiIbBOm38onylTnuXtt9+mZ69eZPXr51FEs9nEoUMH6Xr++bRunVZTjdrpAM5jM3DQIBKTksBrvWfbvuHbtmYTiYlJTJk6Fbvdzj/+8TL/+MfLjBo1ir/9bRq9evemQ/sO/G3aNKY8+yxPPfUUTz31FHfffTfPP/8C6e3a+elmatveOU5iNps5cfw4AF0zuni0etu2bQE4dOggmtuaN//9b8d4Io6JPja7DYPegMls4tDBg2zalMeggQN96tSrVy+UXYHm9szc3fE83EGjgccEEadfazWUpvz4vtahCm6WAH49CuZ9dntYZ3Lefh3Z+1rNv+/Myc2v6fZcJXD6DbGu2mle12WdFazf+oxBuSZQOMuoGmT9NpDbI7b6rdP19l0uTn328N2qhB/fF42MjAyXd/DgQVff4dEgzhYIdizKbUwq+DEot38nJyfTu08fli5dyo4dO+jZs1f9ERiefosWLejatStZWdk89/xzxMXFe+SXkpzMoUOHmPzY/2E2m1m79nsuHDiQX/bs4bbbbnXLS+F5N+q4k2nZsib97Gyef+454uITcJ+ll5ySUnvEag5N/qGDtfl9v46BAy9kz5493HbrrV6t4H6IvVvH/5KOnToB8MQTT/LY5Mke63Q6HcnJyQAM+tVgkpOT2blzJydPnqJnz1506NiJ7t17sHvPbnbv3sPu3buZOHESMbGxjrtqau/IykpK+f1Tv2fD+vV8+ulnXHvttRQVFzP+rjs5fvwEAEZjHB07dmLz5s2Ul1d4JoCiZYsWdD3/fIoKC6mqshAfF4f7HaRzfXZWFs89/zzxcfEeZ3xyUjKg6NypM2/Nns2UKVOYM3cu/3rlFX73u0eYUzNOk52dzUcff8zOnTt5/fXXeWPmTOx2O6/8618kJia5nTW4jjBoriNujDOS3q4dAKdqIh5nQcrLywFo06Yt7tFMQUEB99xzD3FxccydO5errrqaK668kjhjHB07daJPnz78+8036dG9h8cFazQaiYuL8z2w/m5R/S3TvJZ79UeBxkqCGpNy873PzbDHoLytqvUdyToL5u2HaD0awvP41ybf8DGpQO3srQeuvpbAHbp/6zsG5Vzu73jUa+sNoTxt7YMU737YmaqjpAHHoLx9PxFR7ZjTbA4ePOgzJlVXRORoz9qABbdc/eEjUO4THdx9fzY2NpaRI0cxZ/ZsXn7pJfr07kPvPn1cj/++WbaUARcOJL1tWzdVrLl7qRGEdu3a0b1HDzZu3Ehx8Wn69evsU+TDh4+wcuVKpk7NZdCvfgVAtdWK3a5cZXRLmYqKCoevQXp6Tfo/bKSo+DT9OnUO0BS48sx3z2/QIEd+1bX5ud9Ced6haR4peTe8BnTu1JGLLrqIH37YgMVspmOnjn7P9K5dM7jwwgv5cfNmSkpKGDx4MCnJyWRlZ7Fs2VKWL/8OgN59etfeubrKpDh+/Bg///wzw4YP58qrrkSn12OzWbFaba670fj4OHr27MmxY8f4esnXZGVnYTDEoLBjtdpo07Yt53ftytq1a9m0aTNXXnklADa7Y0Zierv29OjenR82bqS4uJhO/Tr7XLAoRUlpKS1SU+mS0ZWnfv8UBQUFbFi/nrLSMtq2aYvJbCY5KYnMvpk8l/sc+/ft59ix41jMFpISk3zaFTQsFgtVlqqa00gjOysbgO/++y3Dhw3DGGektKSM779fS/v27RkwoL/HffQ999zDyy//g6PHjrJhww+8+uoMMjMzadO2Db169uLfs2axfft2hgwZ4nExeRWk9t/+rjC/u7lFQB5+/dYZijQ8QnLi6XsXy+N+17m7t98AW3vgNf++MydvP8D15Gvx72veEVLA5BtkPQ93+BGRh+/V4QcV4nhvHuTurvbBs70a3iAac+fWztabMGGCKwWnSE2dOjWoWXruy/2hf+aZZ6fWsb5eOnfujMFg4O235/PBBx+wf/8+ftzyI3/9y4u8+OKLpKakctHFF3P48GHmzZ3L5VdcwWWXDXXtbzQaMRpjefvt+axYsZxYo5GiwiIWLVrEiuXLybnoIioqKlixfDm7d++mRYtU8jZtYsqzz/Djjz/SrVs3brjhRhLi4yktK+OjxYvZu3cvLVqkUlBQQI+ePYiLi3Olb4wzUlRYyKLFjvQvuvgiNE3nFkBplJWWstyVXws2bdrEs2753XjDjSQkJOB+ClRbrSxduoQjR44wZuwY0tJaA4r8/MPMnTuHK664ksuGXkZKSirlFeXMeuMNNm/eTGJiEkeOHmHunLkcOHiA7Oz+oDmimx0/7WDRokUcOHCA3z36f3Tu3Bm73c67777LhvXrycrK4q7xE2rKUnuwFVBdVc2yZUv54YcfSE5O5sD+A0ydOoVvvvmG9PR2jK0pY4f27di4cSOffPwxhw8fobqqipmvz2TZsmVcO/xakpKS+fDDD1m1chWVFeUcOXKEPzz1FDGGGAYOHEis0cjb8+ezYsUK4oyxFBYVsWjhQpavWMFFORexe/fP3Dx2LHv37UNDY9PmTXz4wYd06XIeY8fezMa8jdxy882Ul5dRba1m9apVfPrppwwcNJBRo0dhiPG6h9Jg/Yb1fPTRYuxKYbXaHELbqyfHjh7l/fff50RBAWazmZf+/ncWLFjAvffdx7hbb0Nv0HM4P585c+Zw3fXXc/XVV9OqZStA8dJLL9GmTWuGDMmhQ4f2bNyYxzvvvI3ZbMZmt7N2zVpmzJjOhRcOJDk5mcJThSxevJhOnToxbNhwYgwxAKxatdL1zkjnzucFf0G5lN3Lj4R1UsctaySz959dY1awASUIM9nwI6y6I4boo/Z4OcVp9uzZTJhwN84GueLyy4Fakbr88stdDRXue1BYvGbx1ffnmt3nNuuvtKxcvf322yozM1PV1Ea1atVKvfDCn1VhUbGyuM3imzo1t3aWn3PWn6VKLVm6TA0aNMi1/3lduqi/v/SSKj5doipNJjV9xgzVqlUrBahBgwapjz76WE2cNEkNGzbMNbuvpLRMPfXUH1xpjBw5Up0oOKksVdV1pu89g6/SZG5Qfu5/JTWz+LKystX2n35yze5b4VZv56y/SpNZvf32O6pnz16u8mRm9lPz5s13zZCrrraq1157TQHqoosuUnv37lXV1VZVcPKkGjlypALU73//lDJbLKq62uqYqedmq6qr1cJFi1TPXo48evbqpea//bb6/VNPecwErLba1Pbt29UNN9zgcexeeullVVpWpixV1erDhQtVZr9+rvWDBg1SS5d945qluXTZMjVo0K9c67t06aJeeulldbqkVB09dlzlPvecqy0BddVVV6sffvhBVVtt6pdf9qrfPvigSk5Odq2/+eab1d69+1R1zcy9aq+ZfN+vW6eys7Nd2y9Y8J6yWm2qoOCkevLJJ11ptWrVSv3pT39SxcWnXfu6z+KzWh0z6U6ePKXGjh2runTpotauXausVpvaf+CAuvPOO115JCcnqwkTJqg9v/yirNbaWXz33XefKisvd83Sy83NVYBauXKlx+y9QH82Hz/QLL/6Z/3VP8uvPtuAWYDNNMsvErP+IjPLr5FnAargZv0pj+W1vl+rAvheM+f8zfrbv3+/grpn6zln93333XcBtwmWiH7N3G63U1JSgt1uJzk5mdjY2KD3Ly0pQeEY3zIYDB53HJUmExaLmaSkZGIMBmfA4xPqlpWVU1VlITkpmVij0TVpw26zU1JaimP2WAoGvd5zR2dONb7JVInZbCE5OQmDIcatJGHcorphtVopKysDIDU1FZ2u/vemg73/rK6qorSsjJTkZGJiY+u8gysvL8fibDfnsavZwa7slJwuAc0xW02n13lk5L4+OSnZM/JRUFVdRVlZGTqdzm9dTSYTlZWVGAwxpKQkuz0G8F8zq9VGWVkpOp2+ZnsdzocXlaZKKitNJCTEkxCfgO8XIfx/GcLf0SwvL8disZCQkEB8vOf4aINoioioSYvj9R5UU0dEzpAlQhFRYyYfndRzvOp4BLn8u++48sorPWbref/swvsAACAASURBVEZCMG/eXMaPHx+x96Dk96DqIezLz1l9twS89TC4M95Pgo3WIdQvwJHsnnxT90NTdPgRPD2dHbq379vh12+de3j7Ea1AE7dv9B2+xmwAzw7fp8f244cz5nRmCGbdyO9B+REMTz/QDm45hqw4jd9DNnoH0BQdWkg1bliBghGIhn6bLyyiTIAb/3A2siB4l8DZo3v4odtwBOPMFJB6jpefWX8NDXB8LCEIlE/xfJS94RGYzy2Bf4XA9xA2XoceLmFfbn70L7I9RKAMGiND3wI4O3RPP2r1q8l7mPqL1/AIrEkVLHCBIyqo4WQXQu1CIMINUmcI5auQzkdqfiMwZ6oBBLRp2ic4oj6CanCH3gDrowDeCdQrkE1wwUe6fYItbRN3OIFrFJkChRMRNYL+NHv7Nn72zaFYfhQsTD3wTj6ciCiaOvygj0+AiChis/Sou318BCqY96DqHYOKWIQUPYRz+Tl3iFiEVGeCkcgg+OPT6PoVKMPGrG4EadwxqAi0aPOePk1//tRLhBukjkkIbj14QEUMZ0yqcdqncYn6CMqboE+XOg6of7106/C9E6hXYCN1SYV+CkXycvJbmmbuwBpeY/8FrJ204OmHM+YU0Qs+SgQiEtn7z64xK9iAEkRSX5yLA+tJg0oX3dRzvBo5wgp+DMqn/w5+zOlciLBCvpzc2jcyCfrJoBk7iKjRr0hWLwx8ixcowvL2IxFhNbOCBaEnkciuAbWLAGGWOMgIq87N69896nvXqIug6j28PgIZ+IA4N3AKaAN3qEMgm/8SaIwLttEzaEJFCS5Ccm51ho85NWlx5D2oRjw8TUA9x6sexas7QnIuDuBT275aAxtI3oOqh7AvP2f13RIIT//8JNhoHUL9itPketYUHX4ET095D6ru4kXf4WvMBvDu8J2LA/vhRERnhmDWTdRFUMES9Onk1r97+P70tM4d3HKM4ll/jd4BNEWHFlKNG1YgeQ+qYcVpvMPZyILgXQJnj+7hh27DEYwzU0DqOV4BxqTkPagoOcRhX25+9C+yPUSgDBojQ98CODt0Tz9q9avJe5j6iyfvQYWaXQi1C4EIN0idIZSvQjofqfmNwJypBhDQpmmf4Ij6CKrBHXoDrI8CeCdQr0A2wQUf6fYJtrRN3OEErlFkCiTvQTV19s2hWH4ULEw98E4+nIgomjr8oI9PgIjI/xiUvAfV7IRz+Tl3iOwsvUAJRiKD4I9Po+tXoAwbs7oRRN6Dqrs4TX7+1EuEG6TOSQjOxYEVMZwxqcZpn8Yl6iMob4I+Xeo4oP710q3D906gXoGN1CUV+ikUycvJb2mauQNreI39F1Deg6q7wI1+/jRqBRtQgkjqi3NxYD1pUOmim3qOVyNHWPIeVIQJ+3Jya9/IJOgng2bsIKJGvyJZvTDwLZ68B+VPTyKRXQNqFwHCLHGQEVadm9e/e9T3rlEXQdV7eH0EMvABcW4g70EF0b82e48QXgHkPajGLo68B9WIh6cJqOd41aN48h5UlBH25eesvlsC4emfnwQbrUOoX3GaXM+aosOP4Okp70HVXbzoO3yN2QDeHb5zcWA/nIjozBDMujFYqqqbuwyCIAiC4IOmlEcAJQiCIAhRga65CyAIgiAI/hCBEgRBEKISEShBEAQhKhGBEgRBEKISEShBEAQhKhGBEgRBEKISEShBEAQhKhGBEgRBEKISEShBEAQhKhGBEgRBEKISEShBEAQhKhGBEgRBEKISEShBEAQhKhGBEgRBEKISEShBEAQhKhGBEgRBEKISEShBEAQhKhGBEgRBEKISEShBEAQhKhGBEgRBEKISEShBEAQhKhGBEgRBEKISEShBEAQhKhGBEgRBEKISEShBEAQhKglboPLy8hg9ejTff/+933X33nsvxcXF4WYjCIIgnGMYwk3AbDbz5ZdfkpCQQO/evWnZsqXHuvz8fOx2e7jZCIIgCOcYEXnEd8MNN2CxWPjPf/4TieQEQRAEITICZTKZuPXWW5k7dy5Hjhypc9uKigoWLFjANddcg6ZpXHPNNfz3v/9FKeXaZvr06SxevJg9e/Ywfvx40tPTmThxIvn5+ZSVlTF16lQyMjIYOnQo69at88lj27ZtjB8/npSUFK655ho+//xzieIEQRDOMCI2SeLSSy+lW7duzJ8/H5vN5ncbu93Oa6+9xjfffENubi4HDhwgJyeHhx56iB07dri2O336NH/+85956qmnGDNmDDNnzmTHjh1MnjyZ++67j/bt2/PWW2/Ro0cPnnnmGY4dO+bad8WKFdx5551cfvnl7Nmzhz/+8Y+8+OKLfP7555GqqiAIgtAEhD0G5SQ+Pp577rmHhx56iFGjRpGVleWzjU6nY/LkyWiahqZpADz66KPk5eWRl5dH3759Xdt26NCBV199lXbt2gFQVlbGCy+8wOzZs7n00ksBaNOmDePGjePgwYO0b9+e0tJSXn/9dR588EHuvvtuNE0jPT2dJ554gk8++YRrrrmGxMTESFVZEARBaEQiOs28f//+jB49mvnz52OxWPxnqNOhaRpVVVXs3LmT7777jrKyMg4ePOixXadOnUhNTXX53bp1IyEhgTZt2riWGY1GYmNjXRHbgQMHOHr0KJdddplLAAEuuOACKisrMZvNkayuIAiC0IhEVKAMBgN33HEHGzduZMWKFX63OXnyJE8//TRZWVlMnz6d8vJyD9EJh7KyMlatWkWfPn1cUZqmafTp04eTJ09itVojko8gCILQ+ETsEZ+TLl26cPvtt/Puu+/ym9/8xmNdcXExDz74IBdccAF5eXkkJiZiMplYv359RPLW6/UMGDCABQsW0Lt374ikKQiCIDQPEf+ShKZpjB07FovFwurVqz3GfAoKCiguLmbcuHGNMhbUpUsX0tLSyMvLi3jagiAIQtPSKJ86atmyJRMnTmTGjBlUVFS4lsfFxVFZWcn69eux2+2Ulpby6quvsmDBgojk265dO+6++27+8pe/8OWXX2K1WrHb7Wzfvp19+/ZFJA9BEAShaWi0b/FddtlljB8/3mPZeeedx8MPP8xjjz2GXq/n0ksvpVOnTvzud7+LSJ6apnHLLbfw9NNPM3nyZGJiYtDr9fz2t79l9+7dEclDEARBaBo05f6GbBNhMpkwm80kJydjMER8GAwAq9VKWVkZAKmpqeh08l1cQRCEM4lmEShBEARBqA8JKwRBEISoRARKEARBiEpEoARBEISoRARKEARBiEpEoARBEISoRARKEARBiEoi+hKS1Q5VNpDfBhQEQTg30ekgVg+GCIQ/ERGoUjOUWxRV/n+nUBAEQTjHiNVDklEjJS70NMJ6UddUDUWVimoRJkEQBMEPMXpolaARHxP8viELVLkFTlXU7poQC4mxEGfQ0MvIliAIwjmJzQ5mq6KiCiqrape3TtRIMgaXVkiP+EzVteKk10FaAiTEanz11VcsWrSIdevWcezYMeQrSoIgCOcGmqbRvn17cnJyGDt2LCNGjKCySlFY6RCtUxUKvS64SCqkCOpIieOxnl4H6ckaJUUnefh3j7Lkq/8Em5QgCIJwFjJ8xEimv/JPUlu14USZwmZ3PO7rmKo1OI2gBarU7Bh3AmibBBUlpxh13Q3s2bWDlBatuP/BR7j5xtGc37WrfEFcEAThHMFut7Nv/34WfvwFM1/9F6Wni+jR+wK+/OwTElNbU1Du2K5VQsMnTgQtUEdLHLP1EmKgbbLGuNtuZ8lX/6H/oIt47+3ZtGvXLth6CYIgCGcRx48f59Y7J/Ljxu8ZPmIk7y94l4IyRWW1Y3ZfhwZGUUEJlNUOh087Nm+TBCu//ZrbbruNlBatWP/9GhEnQRAEAXCI1JCLLqH0dBELFixg6NXXcrImiurUQmvQe1JBPYNzveekFHEGjUWLFgFw/4OPiDgJgiAILtq1a8f9Dz4CwKJFi4gzaFATDzX0ndmgBMr9CxF6Haxbtw6Am28cHUwygiAIwjmAUxvWrVvn8fpRQ782FNYshmPHjgFwfteu4SQjCIIgnIU4tcGpFcESlkA5h69ktp4gCILgjVMbQn0nVpRFEARBiEpEoARBEISoRARKEARBiEpEoARBEISoRATqDEPTNDSt4d+yEgRBOFMRgRIEQRCiEhEoQRAEISoRgRIEQRCiEhEoQRAEISoRgYog06dP55lnnsFsNjd3UQRBEM54QvrJ91ApLi7mySefJDs7mwceeAC9Xu+z7v7772fgwIFNWp5Dhw4F3Gb06NE8/PDDDUrv9OnTnDx5Un7qXhAEIQI0qUDZ7Xby8/NZtmwZOTk5DBo0yGddU0YfCQkJTJo0yZXn8uXL+frrr3n66adJTk4GID09vcnKIwiCINTSpAIFkJqayogRI3jzzTfp27cv8fHxTV0EF0ajkZycHJd/5MgR1q1bx8UXX0xaWlqzlUsQBEFohjGokpISrrzySvbv38+qVavq3T4/P59HH32UtLQ0cnJymD17NhaLBYAvv/ySSZMmUVhY6Np+69at3HjjjWzdutW1rLi4mHvvvZdvvvkm5HIfOHCAxx57jIyMDHr37s20adMoKyurcx+r1co///lPHnnkEYqLiwEwmUzMnDmTrKwsMjIyeOKJJzh+/Lhrn3379vHoo49y9OhR3nvvPXJycsjIyCA3N5eKioqQyy8IgnCm0SyTJDp27Mhdd93Fq6++SkFBQcDtdu7cyS233EL79u3ZsmULM2fO5KOPPmLmzJkopejcuTN5eXkcOHDAtc/333/P+vXr+f77713L8vPz2bNnD507dw6pvDt37mTcuHEAfPHFF8yaNYtVq1Zx7733cvLkSb/7KKX48MMP+eCDD3jggQdo2bIlFRUVPP7446xcuZJ33nmH1atXYzQaPQSsurqa9evXc99997Fp0yb+/Oc/84c//IE5c+awZMmSkMovCIJwJtJss/hGjhyJ0Wjk008/9TupoKqqirlz53LVVVcxefJkOnXqRP/+/Xn22WdZtmwZx48fp0uXLvTo0YNdu3YBjuhk+/btPProo/z444+YTCbAITDnnXceHTp0CLqcFouFt956i169ejF16lQyMzMZOnQo06dP5/Dhw3z22Wd+91u5ciXTpk3jr3/9K3369AEcY1y7d+/m73//O1lZWXTq1InJkyejlGLTpk2ufU0mE+PGjWPatGlcffXVTJgwgeuvv57Vq1cHXX5BEIQzlWYTqJYtW3L//ffzwQcfcPDgQZ/1p06dYsOGDQwfPhyDoXaorGvXriQmJlJaWkpKSgrZ2dnk5eVRVVVFQUEBp06d4pJLLuHUqVMUFBRgs9nYsmULAwYMcE18CIbCwkLy8vIYNmwYKSkpruWdO3dm6NChbNq0yWdix+HDh8nNzeXxxx9n6NChANhsNtasWcMll1ziIZQtWrSgb9++Ho/5nPV0fnPPaDTSunVrKisrgy6/IAjCmUqzvgd1ySWX0L9/f/79739jtVo91pWVlVFcXMzll1/u+kCqpmm0bduWbdu2YbVa0TSNIUOGsG3bNkpKSjh48CDt27cnMzOTtLQ0du/eTWlpKT/99BODBw8OqYzOcmRkZHgs1+v1xMfHU1BQ4BoTAygqKuK5555j165d7Ny5E5vNBjgiwqKiInJzc9HpdK766HQ6cnNz5d0pQRAEL5pVoIxGI3fddRerVq1i+/btHuv0ej0Gg4EVK1aglPL427FjB3379gWgZ8+e2Gw2Dh06RF5eHgMHDiQ1NZUBAwawceNG8vPzsVgsdOrUKaQyOsvhPhEDHGNMNpuNtLQ0YmNjXcsXLlxISkoKX375JWvWrGHlypWA4yvker2eKVOmYLfbfeo0adKkkMonCIJwttLsX5Lo168fY8aM4Z133vGYct6+fXv69evHmjVrXFGIP9q2bUvPnj1Zu3Ytu3btol+/fgBkZmaya9cutmzZQkZGBm3btg2pfM5yrF+/3iPKO336NFu3bqVnz57ExcW5lo8aNYpnnnmG/v37c/vttzNjxgxOnjxJXFwcF154IZs3bw44sUIQBEGopdkFStM0xo4dy8GDB/nkk09cyxMTE5kwYQLz5s1j3rx5mEwmlFIcOHCAH3/80bVdfHw8AwYM4IsvvqCsrMw1vtO1a1fKy8t55513GDBgQMjvWznLsXDhQubNm4fFYqG0tJRXXnmFgoICbrrpJo/fZ+rUqROpqamuehkMBubOnYvNZmP06NFomkZubi7Hjh0DoLS0lNWrV3s8JhQEQRCiQKDAMe38vvvu85nEMHToUF577TVmzpxJQkICOp2O6667ji1btnhEVYMGDWLbtm1kZGTQsmVLAFq3bk2XLl3Ytm2bxxcrQmHo0KHMnDmTN954g7i4OFJTU8nLy+PNN9/0GZtyxzkRZN68eWzcuJH09HSmT59OeXk5HTp0QNM0unbtyqeffkp5eXlYZRQEQTjb0FQQH44rt8CpCgVKkZGmc4mB8x2exsJut1NSUgJAcnKyx6y+psRqtVJWVoZOpyMlJSWsX7YtLy/HYrGQkJAQVHTnzFO+9ycIwpmAu04cKLSDptE6USPJWP++zdPTB4lOVyuGzYnBYIhYOZKSkkhKSopIWoIgCGcjUfGITxAEQRC8EYESBEEQohIRKEEQBCEqOSPGoIRaZHKEIAjnChJBCYIgCFGJCJQgCIIQlYhACYIgCFGJCJQgCIIQlYQlUM6vGtjt9ogURhAEQTh7cGpDqF/dCUug2rdvD8C+/fvDSUYQBEE4C3Fqg1MrgiUogdK5bW21Q05ODgALP/4ipMwFQRCEsxenNuTk5GB1e9Cma6DyBCVQsfraf1uqFWPHjgVg5qv/8vnJckEQBOHc5fjx48x89V8AjB07Fkt17Tuc7lpSF0EJlEFXm3C5RTFixAiGjxhJ6ekibr1zooiUIAiCwPHjx7n1zomUni5i+IiRjBgxgnKLQ6Bi9Q4taQhB/dwGQKkZiiodP1PeNkmjsvQUo667gT27dpDSohX3P/gIN984mvO7dkXX0DhOEARBOKOx2+3s27+fhR9/wcxX/0Xp6SJ69L6ALz/7hISU1hSUKzRNo1WCRkpc/elBCAIFcKREUW1V6DRFuxQdJcWnePh3j7Lkq/8Em5QgCIJwFjJ8xEimv/JPUlu25nipHbvSiDFodExt+Iy+kATKVA0nyhw/XKjTFGmJOhKNGl999RWLFi1i3bp1HDt2TL4bJwiCcI6gaRrt27cnJyeHsWPHMmLECCosisIKhzihaaQna8THBJFmKAIFnr+uq5QiPgaS4nTExYBBF/ovzQqCIAhnLla7wlwN5WY7puqad6CC+BVdd0IWKHBEUkWVjsd9CqAmKYmbBEEQzk1c4YmmoQExBse4UzCRkyuJcATKSanZMauvyirSJAiCIECsQSPJ2PAJEf6IiEA5sdqhygby5SNBEIRzE50uuKnkdRFRgRIEQRCESCEvKgmCIAhRiQiUIAiCEJWIQAmCIAhRiQiUIAiCEJWIQAmCIAhRiVbYqbvM4hMEQRCiDomgBEEQhKhEBEoQBEGISkSgBEEQhKhEBEoQBEGISgzeC1rl72mOcgiCIAjnMEWde/gsi7oIavr06TzzzDOYzebmLoogCILQjPhEUMGSl5fHH//4R49lHTp04MYbb2T48OEYjcH9QtXp06c5efKk/BqvIAjCOU7YAmU2m9m/fz9TpkyhY8eOAOzcuZMXXniBJUuWMG3aNBITE8MuqCAIgnBuEbZAASQkJDBo0CB69eoFwBVXXEFOTg633347P/74I5dcckkkshEEQRDOIRptDOq8887jvPPO48CBA65lBw4c4LHHHiMjI4PevXszbdo0ysrK6k3LarXy0UcfMXToUNLS0pg4cSJ79shkDkEQhLOZRhOoQ4cOcejQIbp16wY4HvuNGzcOgC+++IJZs2axatUq7r33Xk6ePBkwHavVyssvv8wbb7zBCy+8wI4dOxg4cCD33nuvh/gJgiAIZxcRecRnt9spLS2lsLAQu93O1q1bmTZtGldeeSX9+vXDYrHw1ltv0atXL6ZOnUpKSgrgiLLuuOMOPvvsMyZNmuQ37e3bt/Pxxx8za9YssrKyALjnnnvYvXs33377bcD9BEEQhDObiERQ27ZtY/DgwbRu3Zq2bdsyadIkrr32WtcEicLCQvLy8hg2bJhLnAA6d+7M0KFD2bRpU8Bp5evXr6dfv350797dtcxoNDJw4ECOHj0aieILgiAIUUhEBCo7O5tdu3ahlOK9996jdevWjBw50jV7r6ysjOLiYjIyMjz20+v1xMfHU1BQgMVi8Zt2QUEBb775JomJiWia5vobP348FosFu90eiSoIgiAIUUbEx6BGjhxJ3759mT9/PlarFXAIkcFgoLCw0GNbpRQ2m420tDRiY2P9pmcwGLjnnnuoqKhAKeXx98ILL6DTRd27xoIgCEIEiHjvnpKSwqRJk1i8eDFr164FoH379vTr14/169e7RAscL+Vu3bqVnj17EhcX5ze9wYMHs3nzZvLz8yNdVEEQBCGKaZTwY8iQIYwePZoZM2ZQXFxMYmIiEyZMYOHChcybNw+LxUJpaSmvvPIKBQUF3HTTTWia5trfZrO5viSRk5PDkCFD+MMf/sCePXtQSmEymVizZg2lpaWNUXxBEAQhCmgUgTIajUyaNIndu3fz5ZdfopRi6NChzJw5kzfeeIO4uDhSU1PJy8vjzTff9BibysrK4oMPPuCVV14BIDExkeeff56ePXsycOBAdDod6enpzJo1i+Li4sYoviAIghAF+Pzke2N/zdxqtVJWVoZOpyMlJcUjcgLHuFRpaSl6vZ6kpCSPdSaTicrKSoxGo886QRAE4czF39fMI/IeVDAYDAZatmwZcL2maaSmpvpdFx8fT3x8fGMVTRAEQYgiZAqcIAiCEJWIQAmCIAhRiQiUIAiCEJWIQAmCIAhRiQiUIAiCEJWIQAmCIAhRiQiUIAiCEJWIQAmCIAhRiQiUIAiCEJVE9EsSzg+8CoIgCOc23p+xC4WICJRSiqqqaipNFqqqqrHJjwgKgiCck+h1OmJjY0iINxIbGxOWUIUtUEopSssqqKj0/5PtgiAIwrmDzW7HZLZgMltITIgjJTkxZJEKS6C8xSkxMZ6EeCMxhib/Bq0gCIIQBVRbrVSaLFRUmFzaEKpIhawkSikslipXAVq2SCY+zhhqcoIgCMJZQIzBQGqygdgYA8Wny6ioNGOMjcFojA1apEKexaeUotJkARyRk4iTIAiC4CQ+zkhiouPnkSpNlpAm0YUkUEoplFJUV1sBSIgXcRIEQRA8cWpDdbXVpRvBENZ7UM7ZejLmJAiCIHjj1IZQZ3aH9YhPEARBEBpCkz3iEwRBEITGRgRKEARBiEpEoARBEISoRARKEARBiEpEoARBEISoRARKEARBiEpEoARBEISoRARKEARBiEpEoARBEISoRARKEARBiErOKoE6ceIEeXl5WK3W5i6KIAiCECZnlUDNmjWLSZMmsXfvXtey8vJyTCZTM5ZKEARBCIWzSqDuu+8+3nrrLbp16waAzWbjxRdf5KOPPmrmkgmCIEQHhUWnG2XbxuCsEqj09HQGDhyIoeYT71VVVRQVFTVzqQRBEKKDd97/lCl/fqVe4dmz9wAPTc7lnfc/baKS+adZBer111/n6aef9ngEt2TJEu644w7y8/Ndy/bt28edd97J9u3bAZg+fTqLFy9m165d3HTTTQwfPpwTJ07w6aefcu+991JcXMzx48d57LHH+Prrr3n55ZcZPnw4zzzzDGaz4yfqTSYTM2fOJCsri4yMDJ544gmOHz/etA0gCILQhLRqmQpQp0jt2XuAV16bB0CPbl2arGz+aFaB6t69O8uXL6egoABwPJJbuXIly5YtY9euXa7t9u7dy6lTp2jXrh0Ap0+f5t133+Xpp5/mN7/5Dbm5ubRo0YLy8nLy8/Ox2+0kJiZy3XXX0a5dO6677jr+8Ic/MGbMGGJiYqioqODxxx9n5cqVvPPOO6xevRqj0cgjjzxCcXFxs7SFIAhCYzNy+BXk/Ko/4F+kPMUpg5HDr2jyMrrTrALVs2dPNE3j8OHDABQXF3P06FHuv/9+1q9f7/qBq40bNzJgwABatmzp2nfXrl1MmTKF3/zmN+Tk5GA0ev7sfHJyMoMHD6ZFixb06NGDK664gv79+6PX61m+fDm7d+/m73//O1lZWXTq1InJkyejlGLTpk1N1wCCIAhNzB3jrvcrUt7i9Lvfjm+2MjppVoFq27YtmZmZbNiwAYCDBw+i0+m44oor2LFjB6WlpZSVlbFz505ycnLQ6/WufYcOHUqPHj2CztNms7FmzRouueQSOnTo4FreokUL+vbtK4/5BEE46/EWqXU//Bh14gTNLFDx8fEMGDCAHTt2UFlZyU8//URmZib9+vWjurqagwcPcuLECU6ePEmvXr0ikqdz4kRubi46nQ5N09A0DZ1OR25urmuMShAE4WzGXaSckyGiSZwgCmbxDRo0iL1793LkyBG2bt3K4MGDadmyJT169GDnzp3s37+fpKQk1/hTuGiahl6vZ8qUKdjtdpRSHn+TJk2KSD6CIAjRjrtIRZs4ARiauwBdunTBaDSyYcMGCgoK6Nq1K3q9nr59+7JlyxZatmxJdnY2KSkpQaet0+kwGAzYbDbXsri4OC688EI+++wzTp48Sdu2bSNZHUEQhDOKO8Zdz4hhl5PWqkVzF8WHZo+gWrZsyYABA1i8eDGpqamuiRD9+vVj3bp1LF26lCFDhqBpWtBpJyYm0rNnT1atWkVZWRkWiwW73c7o0aPRNI3c3FyOHTsGQGlpKatXr8ZisUS0foIgCNFONIoTRIFA6fV6cnJy+Pjjj8nMzCQ+Ph5wRFZpaWlUVlbSs2fPkNKOjY1lzJgxrFq1ipSUFK699lqOHz9Oeno606dPp7y8nA4dOqBpGl27duXTTz+lvLw8ktUTqyJnswAAEspJREFUBEEQQkQr7NRduS9olb+n3p2UUthsNgpOOaYndmjXunFKFyGqqqooKysjISHBJYBOysvLsVgsftcJgiAI4XH0+CkA2rZugV6vD/g0rKiz76zsZh+DagpiY2NJS0vzuy4pKYmkpKQmLpEgCIJQH83+iE8QBEEQ/CECJQiCIEQlIlCCIAhCVCICJQiCIEQlIlCCIAhCVCICJQiCIEQlIlCCIAhCVCICJQiCIEQlIlCCIAhCVCICJQiCIEQlYQmUXufYvdpqjUhhBEEQhLMHpzY4tSJYQhYoTdMwGBw/wV5pkp+oEARBEDxxaoPBEPgjsXURVgQVHxcLQEWFCZNZREoQBEFwYDJbqKgwAbVaESwhCZSmaWiaRkyMwZVx8ekySsoq5HGfIAjCOUy11UpJWQXFp8sAhzjFxBhcuhEMIf/chqZp6HQ6EhPiUEphtlRTUWFyKaYgCIJwbhNnjCExIQ6dThfSI76wBQogKTGe2BgDZks1VqsNu1L17C0IgiCcjehq5ifEGWOIjY1Br9c3vUCBp0gZjRqxsTHY7XbA8au7giAIwrmDU4ScgqTT6UIWJ4jAL+o6C6FpGkop9Hq9iJMgCMI5ilOMnGNOoYoTgFbYqbuoiSAIghB1yJckBEEQhKhEBEoQBEGISkSgBEEQhKhEBEoQBEGISjQlU+4EQRCEKEQiKEEQBCEqEYESBEEQohIRKEEQBCEqEYESBEEQohIRKEEQBCEqCftbfO7IhEBBEAQBCOsbfE4iIlBKKaqrrVSaLFRVVWO12SKRrCAIgnCGYdDriY2NISHe6PqhwlAJ+z0opRRl5ZWUyw8VCoIgCG4kJcaTnJTQPD+34S1OiQlxJCTEYdDrXT+/4bCgabj53lbWy3pZL+tl/dmwvrraSqXZQkWFyaUNoYpUyBGUUoqqqmoKi0sBaNkimfg4YyhJCYIgCGcZJrOF4tNlAKS1TCE2NiZokQo5glJKUWmyAI7IKc4YS4haJwiCIJxlxBljSUyIo6LSTKXJEtJ4VEjTzJVSKOWYGAGQEB9Xm7GmAZr44osvvvjnuJ+QEAdAdbXVpRvBENYYlHO2nvNn3pUCDYcFxBdffPHFP4d9g14PEPLM7pDGoJRS2Gw2Ck6dBqB9elpImQuCIAhnN8dOFALQtnUL9DUT6BpKxN6DAg0QK1asWLFi3W3oROhTR1pNOcSKFStWrFh3GzqRiaBQaIqaZ5A1uim++OKLL/4574dDRCIozesfTl/TZL2sl/WyXtafy+vDIUJjUG7/rm9bWS/rZb2sl/Xn5PpgidzPbWhixYoVK1asHxsiERuDQuEpn+KLL7744osfBhGLoDQ0HP/XxBdffPHFF9/lh0pEXtRt27plWIUQBEEQzk4KThUDob2oK2NQYsWKFSu2cW2IRHAMSsNzOp8GqNrnkLJe1sv6Zlufn3+Ml155nXvvvp3MC3pFXflk/Vm+PkQiIlDgeNKotBoLNdbLl/Xn3Pry8nI+/OgLPv1iCTt27aZjh3ZcP2o4E+64hZYtWzR7+YJdb7fZqDCZSEpIQNPpmjR/q92OyWQiISEBvU4Lan+r1cq+A4eoslThul6jsH1l/dm3PhwiI1BK4RJSsWJr7IFDh8l98R+UlpYx/vab6dn9fErLyvnk8694YdorPPvU/5Gaktzs5QzG7vllP/+aOZs/P/skLVqkNmn+v3jlHcz+zrtbVfPv5m5HseeWDZUIRVCApuH5rQun721l/bmw3lxVxez57xEXZ+Tvf/4TrVunudYPGdQfS1U1CfFxUVv+QOvLKysxmczUvkbfdPmXV1ZiMpsD5NuA9P1er9HVvrL+LFwfBhF+D0rhKqHLx8uX9efC+p9372XdD5v5+5+fIS2tJUop13qdXkd8fBzKbX+b1ca3361i7rsL2bN3P9dceRn/O/EOMrp0BhT5+UeZv2Ah9959Oxs2bmbeuws5eaqIMTeM5J7xtxIfH+9K32wx8/FnX7Pgg48oK69kxLArmXTXrbROawlofPvdKvKPHGPU8Kt45fXZbN2+g//311w6d2zPuo2beX/RJ3y/Po/u52cw4Y5buPbXV6DXG/hqybe8++HH/LxnHw9P/hMGg4HHHvlfMi/oWW/5vdunylLNug15vL/4M75fv9GR1+23cO2wK9HrdT7t+9WS//Lu+x/x8y/7ePjxZxx5P3wfmX17U1VVxXcr17Lgw4/Zun0nFw0ZyH1330F2Zh80nQ6o+aE45UzN8e/9+w/yyutvcdN1Ixh66UWgFMeOn2T2/Pf45IslZJzXid+MvZ7rRv6a2NhYUIq331tM2zatybygF6//ez5Lvl3BBb178H8P3Uf/fhdEzfkn66Npfejop06dOjWUHZVSVFQ67uYSEuIdzxo1j1nw4p/D/ldL/ktZeQW33PQ/GI3GOre32ezMnv8+Xy9bziMPTOSh+++mrLScWXPeIedXA0hOTqa4qJh33v+ItevzsFptjBt7Az26d+WdBYs577xOdD8/AzQNs8nM3/7xGvsOHOSPjz/CneNuYsfPv/DZl0u4OOdXxBuNbNvxM9+uWM3KNesZPDCb60ddS4/zM/h2xWo++ewrfjP2Bh797SRiY2N56ZWZZF7Qh/M6dUDT6TGZzJSUlnLf3bdzUc4gzu/SmZiYmHrL713/Jd8s5+PPv+bWsdfzyG8nYYyN5aV/zSTzgt507tTBZ3u9pqOyJu97776di3MGkZFxHsaYGGa/8yHvfvAR94y/jUd/ew8VFZX87R+v0q5dOj27dQU0Sk6X8NU333HFZRfTvl06hYVFvDDtX2RlXsD1o4ej1+nYu/8Qj//xOfpl9uG5pyeTM2Qg737wESWlZQ7x0TTWfL+Bz/+zjDXfb2DYNZdz4+hrOXAonx82/sglFw8h3miMivNP/OjxK00OnUhMiEOn09HkvweFkmfaYj1tRWUl7dq2ITY2xnV+VFaaqKquAkDTdCQlJqDX69m9Zy9Lv13BC88+Sa8e3QC4+cbR7D94iDXrNnLzjaNRSmE2Wxg1/GquG/lrNE1jYP9+/LJ3P3mbtnDlZRcRExPDuh82sf/gIf723B9Jb9sGgIn/v71zD46qPOPwc/bsJrubBXLhohKSGsBwDyRyrYIyeEcU7QhWcKR4wRtWZ7y1tnam6h+dVmcKTqutw7TjoCgXEwSttHVq2xmnFJoCDVcdFUII5MJi9pI9OefrH2fPJru5SDY7bXTfZ2bz23d/Z7/vPWfP5t3vXFcu40c//Rl1dYeZO7sKUOzeU8u6nz/HZXNn4nDtoiu47qorE1+gW2++nsNHP+Gfe2uZM3MGYy8uobRkNEP3BZhRMZn8YfZ+oIOHj351/inL56v6cjaNONOXdem7skvfB+oOsXnrdn789GOJeVl953I8HjebtlRz6YypjBhe1DmCUopIOML6VzYwauRwViy7Bd3lIhaLsbV6B3NmVbJ65TJ0XWfUyBE8dO9drH9lA9dffWWinUCenxeefYrhwwsBCATyePyZ5zh+vJ5hk8r/p+uZ6NdD0yWD50FpoqJ96obXNzFrwWJmLVjMynvW8sXxetA0/r2/jvLxYykZMzoxfU5uDlMmlnO6qTmpnTHFF9n/1DWNnBwPBQX5RKLtmEphWhZ7avdTNX0qI+PFCU1j6JAA48eWcaa5OdFOZcVUpky6JCk/l8uF5nJhmib1Daf4+B97aWpu4UxTC+2GQXzCZO1n/o664icsmpZF/clTfLx7L03NzZxpbqE9Fut9OZIcHzh4hJIxo5kcLwxoGpqmMWdmFY2nz1Df0Nitne3v7aK+oZEH77kLn98HQGvwHPsOHOTyebPRdT0xfXHxRfh9PtpC4cT7Lxg1ksDQQCIeEsgjNzeHWF95i2a3pkmG9kFBYnujqCigu3TOBoPEYga58f0Xd61YxneXLeX4iZO8uO7V+A96RXNLK29t3c5bW7eTyv1334lpWYn9VcoZraf0p5QiZhgEg+d4c3M16369oVtbzz/7ZNJ+L6d/p51IJMqmLTW8ubmaaVMmMqtqOoUF+cn9Jv525nE++btcrqR8I+FID30VgKJbXkmakndzcwsXXXgBXm9u0nLxeNx43B6am1uSpv/obx/z3q4P8XjcNLW2UhQfBbW1hQie+5I7vvdQt3kYW1aK0dGR1H7Xz0HFF0ufeYtmt6ZJxo7i0yBpOCdxdsdTJ5dTveN9TjWeZkjgYjTsfZV+fLS2BnFpnYN3XXdz2y038sMn1uL1enttn5S4u28fgPHQmlU8fN+qpF9vqdOnYsQMfvmr12hrC7Fxw8sUFeSjgNNnmmg83dRj//3N34ljKX0Vxs8Hc/rqz/zrupuzwSAdRgfk5nZOoBSWshKHozvvf+fdP/DCT56i7tARXn9jCz94fC0+nxdd19HdOq+/tp6ZVRXn9Xn3xf97/ZN4cMXpkplNfErZhVIp4j+jJM7yuHz8OErHFLN527tEo9FuvrPeoGDalAn85+ARGhoa+24fuvvxF5WC3BwPkydcQt3BIzS3nO0jv65t2a+HwmGOHvuUJTdcQ2H+sB7bRyl0lxvTtDBN1f/843E40dfVFObnJ89fH98n3aVjmhaWaSX8aVMm8ulnX1B/8lTS9Ec/+Qyf18vI4cOT2n/0wXuYM3MGi69dxIn6Bv78l7+DUowoKqJ8XBl7a/djmmbv+acsj54+z8Gw/kk8iOIBkKF9UFr8J5WoqK1FRQWsWrmcD/70ES+t/w2fn6gnEmmn5exZdu/dR1solJh++tQpVEybxC/Wvcrnx0+gFERj7eytPUBbONSlXbr3R3J8xfx5aJrG+lc3cKapBTRoC4XZU7uPWKyj8y0kt6e73eT5/eze8y9isQ6isXZqduzi7W07ktovLRlNQ+NpDh09hmnZmxXPP39bdd1NXp6f3XtqiRkG0fYYNTs/iPfV+3ItLSm2+z7yCaayiMU6mD5tMpfOqODFda9w/EQDlrKo3V/Hb3+3kcXXLmL06AuSll9x8YVomotRo0awYvmt/H7j23z+RT0+v5elS65n2/b3eKfmfaKxdpSC+oZTHDx8rNfl/ZWvi4oOgIwUKIV9noWoaFedWVXByy89T/3JU1x943Kmz7uKuVfeSPWO91n7wGpKSopRSuH15fLI/av5VukYlt6+mgmV85m3cAmbttQQDJ5LtAd07ydl/SsqLOCZJ79POBThsqtupnz65Sy84Tb++OFfCUXCOPtOEo/4+wJ5fu64/VZqdu5i6uyFXHfzClqDQdbcvTKp/bKyUuZ/ezar7nuUSVUL2Fq987zzdzQQ8HPH8nhfsxZy3dIVtJ7t0lcvyzPR95pHmVS5gK01O/F6c3nkgdWMH1fGTctXMbFyAfc+/ARLbriGZd+5Ken7mbr85s6u4pJxZWx8exuRaJRLK6fx7NOP8cbmd6iYvYgJlfNZ88iTHDpyjA7T7HF5Oyr/B0R704GQkdttFBYMHVASwjcf+xBzA13XCeT56e1ciGh7O9FoOzkeD/74EWYD7dPrzcXbdf9MHxiGQSgcIc/vw+Px9DiNUoq2UBhlWQQCAVyuznnpT/7n01d/+nbmtz/t9YRlKdra2gDIi58KIAjp0tJ6DkjvdhuZKVD5doFSJI/oJJZYYoklzu645Wz6BSqj94NyNjtKLLHEEksscSJOk8xei88OREVFRUVFOzVNMjeC6lo6RUVFRUVFE5oeGToPyv6jFKDi2iUWX3zxxRc/S/0BkJkrSWhdREupnVrKZOKLL7744mednw6Z2QelVDwTp5p2NZ0n4osvvvjiZ6efHhm6Fp/Wy3OVEosvvvjii5+9fv/I2AhK00Ap0FCJtGyNx+KLL7744medPxAycpCEfd6VcxfFeKylxOKLL7744medPxAyuA8KQIHSbJVYYokllljiATCgSx21tH5Jh2nGb92dwVOqBEEQhK89pmnRFgrj1nUKC4b0+1JHaY+gNE3D43HTYZrEDAOfnhPfBmnXT03DjlNVfPHFF1/8rPANwwDsOzz3pzA5pD2CsiyLWMygNWhf9djv8+Jx63ZmdopdNPFG8cUXX3zxs8A3DINwpB2AgmEBcnI8uFyufhWqtAoUgGVZmKZJONJOKBwFICfHg8fjRnfJ5j5BEIRsxLQsDKODWMwePeX5vfh9uei6jquftSHtAuWMopwi5VRKQRAEQQDw+3KTilN/N/OlXaAguUgZRgcxw9YO00y3SUEQBOFrjFvX8Xjc5HhsTbc4wQALFHQWKcuyEs+d1wVBEITswSlCTkFyuVxpFyfIQIECuxg5DycWBEEQsg+nGGmalnik3VYmCpSDFCZBEAQBGFBhcsjQxWJtMpGQIAiCIEBG76grCIIgCJlDCpQgCIIwKJECJQiCIAxKpEAJgiAIgxIpUIIgCMKgRAqUIAiCMCiRAiUIgiAMSqRACYIgCIMSKVCCIAjCoOS/nOQ7sq1wapUAAAAASUVORK5CYII=)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "QB5nIcxR8paT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332,
          "referenced_widgets": [
            "9d0f0c215f6e454799af6787903d50d9",
            "e400206420314f4b889eebaa39d7fe28",
            "97820eed8f174e04b7714b994418f379",
            "1860e29bd6ca4bc99ae73ff9d4d7fe8e",
            "83b9b9cf526f4608adbba689bfe1c49c",
            "ea9e0c4435ae4d6cb6094b18b9ec015a",
            "d614baf334884f9aa5269e6f420e1aec",
            "faeb66ee78d746feaba352e01f57a5f5",
            "36b34f65c37d4f81984b5069fb356b58",
            "422e019f5dc6402f9881f58ec3ce6d8a",
            "d2a15a418ffb4d61b0f5b3917a9e9628",
            "1f8d0f4d823e457ba17d549aadd40a8d",
            "8571cd53688a4d83b58d20604f6d23b0",
            "1bd39ddc51854a4e9aaa5efd2e232c23",
            "284078fa39ab47d9af9dd9e1d094f80f",
            "047b79ed2c0c49c1831e278d4e4ccc1f",
            "64682366b70d44938e4429618f1a3e9f"
          ]
        },
        "outputId": "ebee70bd-502e-4c90-b316-1910c138695b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GyWc1x3-o3xG"
      },
      "source": [
        "If you don't want to use a Google Colab or a Jupyter Notebook, you need to use this command instead: `huggingface-cli login`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gc5AfUeFo3xH"
      },
      "source": [
        "3️⃣ We're now ready to push our trained agent to the 🤗 Hub 🔥 using `package_to_hub()` function\n",
        "\n",
        "- Let's create **the model dictionary that contains the hyperparameters and the Q_table**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "FiMqxqVHg0I4"
      },
      "outputs": [],
      "source": [
        "model = {\n",
        "    \"env_id\": env_id,\n",
        "    \"max_steps\": max_steps,\n",
        "    \"n_training_episodes\": n_training_episodes,\n",
        "    \"n_eval_episodes\": n_eval_episodes,\n",
        "    \"eval_seed\": eval_seed,\n",
        "\n",
        "    \"learning_rate\": learning_rate,\n",
        "    \"gamma\": gamma,\n",
        "\n",
        "    \"max_epsilon\": max_epsilon,\n",
        "    \"min_epsilon\": min_epsilon,\n",
        "    \"decay_rate\": decay_rate,\n",
        "\n",
        "    \"qtable\": Qtable_frozenlake\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kld-AEso3xH"
      },
      "source": [
        "Let's fill the `package_to_hub` function:\n",
        "- `repo_id`: the name of the Hugging Face Hub Repository that will be created/updated `\n",
        "(repo_id = {username}/{repo_name})`\n",
        "💡 **A good name is {username}/q-{env_id}**\n",
        "- `model`: our model dictionary containing the hyperparameters and the Qtable.\n",
        "- `env`: the environment.\n",
        "- `commit_message`: message of the commit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "5sBo2umnXpPd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6307159d-1a3c-489a-ee9e-5a1a9b0703a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'env_id': 'FrozenLake-v1',\n",
              " 'max_steps': 99,\n",
              " 'n_training_episodes': 10000,\n",
              " 'n_eval_episodes': 100,\n",
              " 'eval_seed': [],\n",
              " 'learning_rate': 0.7,\n",
              " 'gamma': 0.95,\n",
              " 'max_epsilon': 1.0,\n",
              " 'min_epsilon': 0.05,\n",
              " 'decay_rate': 0.0005,\n",
              " 'qtable': array([[0.73509189, 0.77378094, 0.77378094, 0.73509189],\n",
              "        [0.73509189, 0.        , 0.81450625, 0.77378094],\n",
              "        [0.77378094, 0.857375  , 0.77378094, 0.81450625],\n",
              "        [0.81450625, 0.        , 0.77378094, 0.77378094],\n",
              "        [0.77378094, 0.81450625, 0.        , 0.73509189],\n",
              "        [0.        , 0.        , 0.        , 0.        ],\n",
              "        [0.        , 0.9025    , 0.        , 0.81450625],\n",
              "        [0.        , 0.        , 0.        , 0.        ],\n",
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          "text": [
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/brouthen/GDG_QAADA_Discover_Reinforcement_Learning into local empty directory.\n"
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            "       [0.        , 0.        , 0.        , 0.        ]]), 'map_name': '4x4', 'slippery': False}\n"
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              "  0%|          | 0/100 [00:00<?, ?it/s]"
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          "text": [
            "Pushing repo GDG_QAADA_Discover_Reinforcement_Learning to the Hugging Face Hub\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:huggingface_hub.repository:remote: Scanning LFS files of refs/heads/main for validity...        \n",
            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/brouthen/GDG_QAADA_Discover_Reinforcement_Learning\n",
            "   3db8acc..f764e57  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your model is pushed to the hub. You can view your model here: https://huggingface.co/brouthen/GDG_QAADA_Discover_Reinforcement_Learning\n"
          ]
        }
      ],
      "source": [
        "username = \"brouthen\" # FILL THIS\n",
        "repo_name = \"GDG_QAADA_Discover_Reinforcement_Learning\"\n",
        "push_to_hub(\n",
        "    repo_id=f\"{username}/{repo_name}\",\n",
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    },
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      "source": [
        "Congrats 🥳 you've just implemented from scratch, trained and uploaded your first Reinforcement Learning agent. \n",
        "FrozenLake-v1 no_slippery is very simple environment, be creative with new ones 🔥."
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    }
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