teachingMarioNew-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "da8cc325",
   "metadata": {},
   "source": [
    "# Setup Mario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac7a4ca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym_super_mario_bros\n",
    "from nes_py.wrappers import JoypadSpace\n",
    "from gym_super_mario_bros.actions import SIMPLE_MOVEMENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0bf60ba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['NOOP'],\n",
       " ['right'],\n",
       " ['right', 'A'],\n",
       " ['right', 'B'],\n",
       " ['right', 'A', 'B'],\n",
       " ['A'],\n",
       " ['left']]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SIMPLE_MOVEMENT # all actions that our ai will take"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "64964a5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joy/.local/lib/python3.10/site-packages/gym/envs/registration.py:593: UserWarning: \u001b[33mWARN: The environment SuperMarioBros-v0 is out of date. You should consider upgrading to version `v3`.\u001b[0m\n",
      "  logger.warn(\n",
      "/home/joy/.local/lib/python3.10/site-packages/gym/core.py:329: 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",
      "/home/joy/.local/lib/python3.10/site-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"
     ]
    }
   ],
   "source": [
    "# Setup Game\n",
    "env = gym_super_mario_bros.make('SuperMarioBros-v0')\n",
    "env = JoypadSpace(env, SIMPLE_MOVEMENT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6d8640ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 256, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.observation_space.shape # A frame from the game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c53958cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discrete(7)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.action_space # All possible actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b23d1cac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:174: UserWarning: \u001b[33mWARN: Future gym versions will require that `Env.reset` can be passed a `seed` instead of using `Env.seed` for resetting the environment random number generator.\u001b[0m\n",
      "  logger.warn(\n",
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:190: UserWarning: \u001b[33mWARN: Future gym versions will require that `Env.reset` can be passed `return_info` to return information from the environment resetting.\u001b[0m\n",
      "  logger.warn(\n",
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:195: UserWarning: \u001b[33mWARN: Future gym versions will require that `Env.reset` can be passed `options` to allow the environment initialisation to be passed additional information.\u001b[0m\n",
      "  logger.warn(\n",
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:227: DeprecationWarning: \u001b[33mWARN: Core environment is written in old step API which returns one bool instead of two. It is recommended to rewrite the environment with new step API. \u001b[0m\n",
      "  logger.deprecation(\n",
      "/home/joy/.local/lib/python3.10/site-packages/gym/core.py:57: DeprecationWarning: \u001b[33mWARN: You are calling render method, but you didn't specified the argument render_mode at environment initialization. To maintain backward compatibility, the environment will render in human mode.\n",
      "If you want to render in human mode, initialize the environment in this way: gym.make('EnvName', render_mode='human') and don't call the render method.\n",
      "See here for more information: https://www.gymlibrary.ml/content/api/\u001b[0m\n",
      "  deprecation(\n",
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:280: UserWarning: \u001b[33mWARN: No render modes was declared in the environment (env.metadata['render_modes'] is None or not defined), you may have trouble when calling `.render()`.\u001b[0m\n",
      "  logger.warn(\n",
      "/home/joy/.local/lib/python3.10/site-packages/gym_super_mario_bros/smb_env.py:148: RuntimeWarning: overflow encountered in ubyte_scalars\n",
      "  return (self.ram[0x86] - self.ram[0x071c]) % 256\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [7], line 11\u001b[0m\n\u001b[1;32m      9\u001b[0m     step_result \u001b[38;5;241m=\u001b[39m env\u001b[38;5;241m.\u001b[39mstep(env\u001b[38;5;241m.\u001b[39maction_space\u001b[38;5;241m.\u001b[39msample())\n\u001b[1;32m     10\u001b[0m     state, reward, done, info \u001b[38;5;241m=\u001b[39m step_result[:\u001b[38;5;241m4\u001b[39m]\n\u001b[0;32m---> 11\u001b[0m     env\u001b[38;5;241m.\u001b[39mrender()\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# Close the game\u001b[39;00m\n\u001b[1;32m     13\u001b[0m env\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py:66\u001b[0m, in \u001b[0;36m_EnvDecorator._deprecate_mode.<locals>.render\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrender_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mkeys():  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m     57\u001b[0m     deprecation(\n\u001b[1;32m     58\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are calling render method, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     59\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut you didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt specified the argument render_mode at environment initialization. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSee here for more information: https://www.gymlibrary.ml/content/api/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     64\u001b[0m     )\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrender_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py:433\u001b[0m, in \u001b[0;36mWrapper.render\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrender\u001b[39m(\n\u001b[1;32m    430\u001b[0m     \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    431\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Union[RenderFrame, List[RenderFrame]]]:\n\u001b[1;32m    432\u001b[0m     \u001b[38;5;124;03m\"\"\"Renders the environment.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 433\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py:66\u001b[0m, in \u001b[0;36m_EnvDecorator._deprecate_mode.<locals>.render\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrender_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mkeys():  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m     57\u001b[0m     deprecation(\n\u001b[1;32m     58\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are calling render method, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     59\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut you didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt specified the argument render_mode at environment initialization. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSee here for more information: https://www.gymlibrary.ml/content/api/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     64\u001b[0m     )\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrender_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py:433\u001b[0m, in \u001b[0;36mWrapper.render\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrender\u001b[39m(\n\u001b[1;32m    430\u001b[0m     \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    431\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Union[RenderFrame, List[RenderFrame]]]:\n\u001b[1;32m    432\u001b[0m     \u001b[38;5;124;03m\"\"\"Renders the environment.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 433\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py:66\u001b[0m, in \u001b[0;36m_EnvDecorator._deprecate_mode.<locals>.render\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrender_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mkeys():  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m     57\u001b[0m     deprecation(\n\u001b[1;32m     58\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are calling render method, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     59\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut you didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt specified the argument render_mode at environment initialization. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSee here for more information: https://www.gymlibrary.ml/content/api/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     64\u001b[0m     )\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrender_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/wrappers/order_enforcing.py:51\u001b[0m, in \u001b[0;36mOrderEnforcing.render\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_disable_render_order_enforcing \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_reset:\n\u001b[1;32m     47\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ResetNeeded(\n\u001b[1;32m     48\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot call `env.render()` before calling `env.reset()`, if this is a intended action, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     49\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mset `disable_render_order_enforcing=True` on the OrderEnforcer wrapper.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     50\u001b[0m     )\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py:66\u001b[0m, in \u001b[0;36m_EnvDecorator._deprecate_mode.<locals>.render\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrender_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mkeys():  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m     57\u001b[0m     deprecation(\n\u001b[1;32m     58\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are calling render method, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     59\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut you didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt specified the argument render_mode at environment initialization. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSee here for more information: https://www.gymlibrary.ml/content/api/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     64\u001b[0m     )\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrender_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py:433\u001b[0m, in \u001b[0;36mWrapper.render\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrender\u001b[39m(\n\u001b[1;32m    430\u001b[0m     \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    431\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[Union[RenderFrame, List[RenderFrame]]]:\n\u001b[1;32m    432\u001b[0m     \u001b[38;5;124;03m\"\"\"Renders the environment.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 433\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py:66\u001b[0m, in \u001b[0;36m_EnvDecorator._deprecate_mode.<locals>.render\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrender_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mkeys():  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m     57\u001b[0m     deprecation(\n\u001b[1;32m     58\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are calling render method, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     59\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut you didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt specified the argument render_mode at environment initialization. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSee here for more information: https://www.gymlibrary.ml/content/api/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     64\u001b[0m     )\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrender_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/wrappers/env_checker.py:55\u001b[0m, in \u001b[0;36mPassiveEnvChecker.render\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m env_render_passive_checker(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39menv, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 55\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrender\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py:66\u001b[0m, in \u001b[0;36m_EnvDecorator._deprecate_mode.<locals>.render\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrender_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspec\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mkeys():  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m     57\u001b[0m     deprecation(\n\u001b[1;32m     58\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are calling render method, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     59\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut you didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt specified the argument render_mode at environment initialization. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSee here for more information: https://www.gymlibrary.ml/content/api/\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     64\u001b[0m     )\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrender_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/nes_py/nes_env.py:379\u001b[0m, in \u001b[0;36mNESEnv.render\u001b[0;34m(self, mode)\u001b[0m\n\u001b[1;32m    373\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mviewer \u001b[38;5;241m=\u001b[39m ImageViewer(\n\u001b[1;32m    374\u001b[0m             caption\u001b[38;5;241m=\u001b[39mcaption,\n\u001b[1;32m    375\u001b[0m             height\u001b[38;5;241m=\u001b[39mSCREEN_HEIGHT,\n\u001b[1;32m    376\u001b[0m             width\u001b[38;5;241m=\u001b[39mSCREEN_WIDTH,\n\u001b[1;32m    377\u001b[0m         )\n\u001b[1;32m    378\u001b[0m     \u001b[38;5;66;03m# show the screen on the image viewer\u001b[39;00m\n\u001b[0;32m--> 379\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mviewer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscreen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    380\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrgb_array\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    381\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscreen\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/nes_py/_image_viewer.py:144\u001b[0m, in \u001b[0;36mImageViewer.show\u001b[0;34m(self, frame)\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_window\u001b[38;5;241m.\u001b[39mdispatch_events()\n\u001b[1;32m    139\u001b[0m \u001b[38;5;66;03m# create an image data object\u001b[39;00m\n\u001b[1;32m    140\u001b[0m image \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpyglet\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mImageData(\n\u001b[1;32m    141\u001b[0m     frame\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m    142\u001b[0m     frame\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRGB\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m--> 144\u001b[0m     \u001b[43mframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtobytes\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m    145\u001b[0m     pitch\u001b[38;5;241m=\u001b[39mframe\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m3\u001b[39m\n\u001b[1;32m    146\u001b[0m )\n\u001b[1;32m    147\u001b[0m \u001b[38;5;66;03m# send the image to the window\u001b[39;00m\n\u001b[1;32m    148\u001b[0m image\u001b[38;5;241m.\u001b[39mblit(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, width\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_window\u001b[38;5;241m.\u001b[39mwidth, height\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_window\u001b[38;5;241m.\u001b[39mheight)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Creating a flag - restart or not \n",
    "done = True\n",
    "for step in range(100000): # This loop iterates through every frame of the game.\n",
    "    if done:\n",
    "        # Start the game\n",
    "        env.reset()\n",
    "        \n",
    "    # perform any random action from our action space\n",
    "    step_result = env.step(env.action_space.sample())\n",
    "    state, reward, done, info = step_result[:4]\n",
    "    env.render()\n",
    "# Close the game\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c0584fb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5c152053",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:174: UserWarning: \u001b[33mWARN: Future gym versions will require that `Env.reset` can be passed a `seed` instead of using `Env.seed` for resetting the environment random number generator.\u001b[0m\n",
      "  logger.warn(\n",
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:190: UserWarning: \u001b[33mWARN: Future gym versions will require that `Env.reset` can be passed `return_info` to return information from the environment resetting.\u001b[0m\n",
      "  logger.warn(\n",
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:195: UserWarning: \u001b[33mWARN: Future gym versions will require that `Env.reset` can be passed `options` to allow the environment initialisation to be passed additional information.\u001b[0m\n",
      "  logger.warn(\n"
     ]
    }
   ],
   "source": [
    "state = env.reset() # the frames of the game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e38b1da1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(240, 256, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state.shape # the frame from the game that is 240px wide, 256 px high, 3 channels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb98cb36",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:227: DeprecationWarning: \u001b[33mWARN: Core environment is written in old step API which returns one bool instead of two. It is recommended to rewrite the environment with new step API. \u001b[0m\n",
      "  logger.deprecation(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'coins': 0,\n",
       " 'flag_get': False,\n",
       " 'life': 2,\n",
       " 'score': 0,\n",
       " 'stage': 1,\n",
       " 'status': 'small',\n",
       " 'time': 400,\n",
       " 'world': 1,\n",
       " 'x_pos': 40,\n",
       " 'x_pos_screen': 40,\n",
       " 'y_pos': 79}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.step(1)[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e67da87c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-21 12:13:19.813879: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-10-21 12:13:19.975323: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-10-21 12:13:20.921044: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "# Import FrameStacker wrapper and Grayscaling wrapper\n",
    "from gym.wrappers import GrayScaleObservation, FrameStack\n",
    "# Importing Vectorization Wrappers\n",
    "from stable_baselines3.common.vec_env import VecFrameStack, DummyVecEnv\n",
    "from stable_baselines3.common.vec_env.base_vec_env import VecEnvWrapper\n",
    "from stable_baselines3.common.vec_env import VecNormalize\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4fa29fc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joy/.local/lib/python3.10/site-packages/gym/envs/registration.py:593: UserWarning: \u001b[33mWARN: The environment SuperMarioBros-v0 is out of date. You should consider upgrading to version `v3`.\u001b[0m\n",
      "  logger.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Box(0, 255, (240, 256, 4), uint8)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joy/.local/lib/python3.10/site-packages/gym/core.py:329: 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",
      "/home/joy/.local/lib/python3.10/site-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",
      "/home/joy/.local/lib/python3.10/site-packages/stable_baselines3/common/vec_env/patch_gym.py:49: UserWarning: You provided an OpenAI Gym environment. We strongly recommend transitioning to Gymnasium environments. Stable-Baselines3 is automatically wrapping your environments in a compatibility layer, which could potentially cause issues.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# 1. Create the base environment\n",
    "env = gym_super_mario_bros.make(\"SuperMarioBros-v0\")\n",
    "# 2. Simplify the controls\n",
    "env = JoypadSpace(env, SIMPLE_MOVEMENT)\n",
    "# 3. Grayscale\n",
    "env = GrayScaleObservation(env, keep_dim = True) # Grayscaling the frames.\n",
    "# 4. Wrap inside the dummy environment\n",
    "\n",
    "env = DummyVecEnv([lambda: env])  #(Different from og code, getting error)\n",
    "\n",
    "# 5. Stack the frames\n",
    "env = VecFrameStack(env, 4, channels_order='last') # (Different from og code, getting error)\n",
    "\n",
    "print(env.observation_space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "41124f88",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:174: UserWarning: \u001b[33mWARN: Future gym versions will require that `Env.reset` can be passed a `seed` instead of using `Env.seed` for resetting the environment random number generator.\u001b[0m\n",
      "  logger.warn(\n",
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:190: UserWarning: \u001b[33mWARN: Future gym versions will require that `Env.reset` can be passed `return_info` to return information from the environment resetting.\u001b[0m\n",
      "  logger.warn(\n",
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:195: UserWarning: \u001b[33mWARN: Future gym versions will require that `Env.reset` can be passed `options` to allow the environment initialisation to be passed additional information.\u001b[0m\n",
      "  logger.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1, 240, 256, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state = env.reset()\n",
    "state.shape # Now, there will only be one channel instead of 3 because we have grayscaled the frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "139c2291",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joy/.local/lib/python3.10/site-packages/gym/utils/passive_env_checker.py:227: DeprecationWarning: \u001b[33mWARN: Core environment is written in old step API which returns one bool instead of two. It is recommended to rewrite the environment with new step API. \u001b[0m\n",
      "  logger.deprecation(\n"
     ]
    }
   ],
   "source": [
    "state, reward, done, info = env.step([5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "73bcf896",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6d643f4220>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(state[0]) # Below is the grayscaled version of our Mario env's frame."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09f5f6b6",
   "metadata": {},
   "source": [
    "## Reinforcement Learning : \n",
    "The type of AI that we are using is called Reinforcement Learning. \n",
    "It follows four key elements: (A.R.E.A) \n",
    "1) Agent : Here, mario acts as our agent.\n",
    "\n",
    "2) Reward : Here, the reward function aims at moving left as far as possible as quickly as possible.\n",
    "\n",
    "3) Environment : Entire game env that we have setup.\n",
    "\n",
    "4) Action : Moving right, jumping, moving left are all the actions as defined in the action space, we have 7 actions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fe0e06f",
   "metadata": {},
   "source": [
    "## Algorithm\n",
    "\n",
    "The specific RL algorithm that we are using is called PPO: Proximal Policy Optimization. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f4e8e7",
   "metadata": {},
   "source": [
    "# Training the RL Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6ab08a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing os for file management\n",
    "import os\n",
    "# Importing the PPO algorithm\n",
    "from stable_baselines3 import PPO\n",
    "# Importing base_callbacks for saving the models\n",
    "from stable_baselines3.common.callbacks import BaseCallback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9dad1e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "CHECKPOINT_DIR = './train/'\n",
    "LOG_DIR = './logs/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "32f3ec7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For saving our model locally every few steps\n",
    "class TrainAndLoggingCallback(BaseCallback):\n",
    "\n",
    "    def __init__(self, check_freq, save_path, verbose=1):\n",
    "        super(TrainAndLoggingCallback, self).__init__(verbose)\n",
    "        self.check_freq = check_freq\n",
    "        self.save_path = save_path\n",
    "\n",
    "    def _init_callback(self):\n",
    "        if self.save_path is not None:\n",
    "            os.makedirs(self.save_path, exist_ok=True)\n",
    "\n",
    "    def _on_step(self):\n",
    "        if self.n_calls % self.check_freq == 0:\n",
    "            model_path = os.path.join(self.save_path, 'best_model_{}'.format(self.n_calls))\n",
    "            self.model.save(model_path)\n",
    "\n",
    "        return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e36b141d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup model saving callback\n",
    "callback = TrainAndLoggingCallback(check_freq=10000, save_path=CHECKPOINT_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "93a2217c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n"
     ]
    }
   ],
   "source": [
    "# This is the AI model started\n",
    "model = PPO('CnnPolicy', env, verbose=1, tensorboard_log=LOG_DIR, learning_rate=0.000001, n_steps=512)\n",
    "\n",
    "# 1. 'CnnPolicy is the neural network that we are using, consider it as the brain of our AI.'\n",
    "# 2. 'env' is loading our game environment into the AI model.\n",
    "# 3. 'verbose = 1' provides us the below information about the training process.\n",
    "# 4. 'tensorboard_log' stores the history of all the training in the path specified (log folder).\n",
    "# 5. 'learning_rate' defines how fast our model will learn.\n",
    "# 6. 'n_steps' defines how many frames we are going to wait per game before we update our neural network with new info. regarding new frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b983d00a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logging to ./logs/PPO_2\n",
      "----------------------------\n",
      "| time/              |     |\n",
      "|    fps             | 100 |\n",
      "|    iterations      | 1   |\n",
      "|    time_elapsed    | 5   |\n",
      "|    total_timesteps | 512 |\n",
      "----------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 21            |\n",
      "|    iterations           | 2             |\n",
      "|    time_elapsed         | 48            |\n",
      "|    total_timesteps      | 1024          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.1567801e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.95         |\n",
      "|    explained_variance   | 0.000306      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 117           |\n",
      "|    n_updates            | 10            |\n",
      "|    policy_gradient_loss | -6.45e-05     |\n",
      "|    value_loss           | 324           |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 16            |\n",
      "|    iterations           | 3             |\n",
      "|    time_elapsed         | 90            |\n",
      "|    total_timesteps      | 1536          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 4.4594286e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.95         |\n",
      "|    explained_variance   | -0.0151       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.235         |\n",
      "|    n_updates            | 20            |\n",
      "|    policy_gradient_loss | -0.000449     |\n",
      "|    value_loss           | 1.63          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 15            |\n",
      "|    iterations           | 4             |\n",
      "|    time_elapsed         | 133           |\n",
      "|    total_timesteps      | 2048          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.4720794e-06 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.95         |\n",
      "|    explained_variance   | -0.00316      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.113         |\n",
      "|    n_updates            | 30            |\n",
      "|    policy_gradient_loss | 1.55e-06      |\n",
      "|    value_loss           | 0.759         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 14            |\n",
      "|    iterations           | 5             |\n",
      "|    time_elapsed         | 175           |\n",
      "|    total_timesteps      | 2560          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.2435019e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.95         |\n",
      "|    explained_variance   | 0.0261        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.202         |\n",
      "|    n_updates            | 40            |\n",
      "|    policy_gradient_loss | -0.000242     |\n",
      "|    value_loss           | 0.697         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 14           |\n",
      "|    iterations           | 6            |\n",
      "|    time_elapsed         | 217          |\n",
      "|    total_timesteps      | 3072         |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 8.227304e-06 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.95        |\n",
      "|    explained_variance   | 0.00499      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.226        |\n",
      "|    n_updates            | 50           |\n",
      "|    policy_gradient_loss | -0.000157    |\n",
      "|    value_loss           | 0.568        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 13            |\n",
      "|    iterations           | 7             |\n",
      "|    time_elapsed         | 259           |\n",
      "|    total_timesteps      | 3584          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 6.2121544e-06 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.95         |\n",
      "|    explained_variance   | 0.0199        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.114         |\n",
      "|    n_updates            | 60            |\n",
      "|    policy_gradient_loss | -9.12e-05     |\n",
      "|    value_loss           | 0.308         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 13            |\n",
      "|    iterations           | 8             |\n",
      "|    time_elapsed         | 301           |\n",
      "|    total_timesteps      | 4096          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.9338448e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.95         |\n",
      "|    explained_variance   | -0.0127       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.109         |\n",
      "|    n_updates            | 70            |\n",
      "|    policy_gradient_loss | -0.000241     |\n",
      "|    value_loss           | 0.283         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 13           |\n",
      "|    iterations           | 9            |\n",
      "|    time_elapsed         | 342          |\n",
      "|    total_timesteps      | 4608         |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 6.529619e-06 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.95        |\n",
      "|    explained_variance   | 0.0117       |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.119        |\n",
      "|    n_updates            | 80           |\n",
      "|    policy_gradient_loss | -0.000121    |\n",
      "|    value_loss           | 0.291        |\n",
      "------------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 13          |\n",
      "|    iterations           | 10          |\n",
      "|    time_elapsed         | 384         |\n",
      "|    total_timesteps      | 5120        |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 2.38996e-05 |\n",
      "|    clip_fraction        | 0           |\n",
      "|    clip_range           | 0.2         |\n",
      "|    entropy_loss         | -1.95       |\n",
      "|    explained_variance   | -0.000897   |\n",
      "|    learning_rate        | 1e-06       |\n",
      "|    loss                 | 0.164       |\n",
      "|    n_updates            | 90          |\n",
      "|    policy_gradient_loss | -0.000476   |\n",
      "|    value_loss           | 0.311       |\n",
      "-----------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 13            |\n",
      "|    iterations           | 11            |\n",
      "|    time_elapsed         | 426           |\n",
      "|    total_timesteps      | 5632          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 6.2802574e-06 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.95         |\n",
      "|    explained_variance   | -0.00626      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.15          |\n",
      "|    n_updates            | 100           |\n",
      "|    policy_gradient_loss | -9.08e-05     |\n",
      "|    value_loss           | 0.296         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 13            |\n",
      "|    iterations           | 12            |\n",
      "|    time_elapsed         | 468           |\n",
      "|    total_timesteps      | 6144          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.7475104e-06 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.95         |\n",
      "|    explained_variance   | 0.00767       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0702        |\n",
      "|    n_updates            | 110           |\n",
      "|    policy_gradient_loss | -2.33e-05     |\n",
      "|    value_loss           | 0.183         |\n",
      "-------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 13            |\n",
      "|    iterations           | 13            |\n",
      "|    time_elapsed         | 510           |\n",
      "|    total_timesteps      | 6656          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.7445302e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.95         |\n",
      "|    explained_variance   | 0.00677       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0883        |\n",
      "|    n_updates            | 120           |\n",
      "|    policy_gradient_loss | -0.000302     |\n",
      "|    value_loss           | 0.198         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 14           |\n",
      "|    time_elapsed         | 552          |\n",
      "|    total_timesteps      | 7168         |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 8.637784e-06 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.95        |\n",
      "|    explained_variance   | 0.00922      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0572       |\n",
      "|    n_updates            | 130          |\n",
      "|    policy_gradient_loss | -0.000108    |\n",
      "|    value_loss           | 0.142        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 15            |\n",
      "|    time_elapsed         | 594           |\n",
      "|    total_timesteps      | 7680          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 7.1949326e-06 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.95         |\n",
      "|    explained_variance   | -0.00887      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0924        |\n",
      "|    n_updates            | 140           |\n",
      "|    policy_gradient_loss | -0.000114     |\n",
      "|    value_loss           | 0.175         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 16            |\n",
      "|    time_elapsed         | 636           |\n",
      "|    total_timesteps      | 8192          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.2647128e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.95         |\n",
      "|    explained_variance   | 0.00416       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0979        |\n",
      "|    n_updates            | 150           |\n",
      "|    policy_gradient_loss | -0.000187     |\n",
      "|    value_loss           | 0.17          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 17            |\n",
      "|    time_elapsed         | 678           |\n",
      "|    total_timesteps      | 8704          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.3511122e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.95         |\n",
      "|    explained_variance   | 0.026         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 149           |\n",
      "|    n_updates            | 160           |\n",
      "|    policy_gradient_loss | -0.000228     |\n",
      "|    value_loss           | 294           |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 18           |\n",
      "|    time_elapsed         | 720          |\n",
      "|    total_timesteps      | 9216         |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 4.059635e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.95        |\n",
      "|    explained_variance   | 0.0113       |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 44.3         |\n",
      "|    n_updates            | 170          |\n",
      "|    policy_gradient_loss | -3.89e-05    |\n",
      "|    value_loss           | 255          |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 19            |\n",
      "|    time_elapsed         | 762           |\n",
      "|    total_timesteps      | 9728          |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.8114144e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | 0.209         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.187         |\n",
      "|    n_updates            | 180           |\n",
      "|    policy_gradient_loss | -0.00023      |\n",
      "|    value_loss           | 1.35          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 20           |\n",
      "|    time_elapsed         | 805          |\n",
      "|    total_timesteps      | 10240        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 7.752562e-06 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.94        |\n",
      "|    explained_variance   | 0.0338       |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.129        |\n",
      "|    n_updates            | 190          |\n",
      "|    policy_gradient_loss | -6.25e-05    |\n",
      "|    value_loss           | 1.05         |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 21            |\n",
      "|    time_elapsed         | 847           |\n",
      "|    total_timesteps      | 10752         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.3921253e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | 0.00107       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.15          |\n",
      "|    n_updates            | 200           |\n",
      "|    policy_gradient_loss | -0.000273     |\n",
      "|    value_loss           | 0.774         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 22            |\n",
      "|    time_elapsed         | 889           |\n",
      "|    total_timesteps      | 11264         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.8160284e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | 0.0178        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.196         |\n",
      "|    n_updates            | 210           |\n",
      "|    policy_gradient_loss | -0.000395     |\n",
      "|    value_loss           | 0.694         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 23            |\n",
      "|    time_elapsed         | 931           |\n",
      "|    total_timesteps      | 11776         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.5177607e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | -0.0108       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.144         |\n",
      "|    n_updates            | 220           |\n",
      "|    policy_gradient_loss | -0.000354     |\n",
      "|    value_loss           | 0.368         |\n",
      "-------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 24            |\n",
      "|    time_elapsed         | 973           |\n",
      "|    total_timesteps      | 12288         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.3188925e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | 0.00112       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.122         |\n",
      "|    n_updates            | 230           |\n",
      "|    policy_gradient_loss | -0.00022      |\n",
      "|    value_loss           | 0.324         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 25            |\n",
      "|    time_elapsed         | 1015          |\n",
      "|    total_timesteps      | 12800         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 5.4466072e-06 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | 0.0046        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.169         |\n",
      "|    n_updates            | 240           |\n",
      "|    policy_gradient_loss | -4.21e-07     |\n",
      "|    value_loss           | 0.313         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 26            |\n",
      "|    time_elapsed         | 1057          |\n",
      "|    total_timesteps      | 13312         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.3685902e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | 0.0115        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.197         |\n",
      "|    n_updates            | 250           |\n",
      "|    policy_gradient_loss | -0.000164     |\n",
      "|    value_loss           | 0.381         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 27           |\n",
      "|    time_elapsed         | 1099         |\n",
      "|    total_timesteps      | 13824        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 3.358815e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.94        |\n",
      "|    explained_variance   | 0.00298      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.151        |\n",
      "|    n_updates            | 260          |\n",
      "|    policy_gradient_loss | -0.000433    |\n",
      "|    value_loss           | 0.249        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 28            |\n",
      "|    time_elapsed         | 1141          |\n",
      "|    total_timesteps      | 14336         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.1557167e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | -0.000942     |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0738        |\n",
      "|    n_updates            | 270           |\n",
      "|    policy_gradient_loss | -0.000382     |\n",
      "|    value_loss           | 0.171         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 29           |\n",
      "|    time_elapsed         | 1183         |\n",
      "|    total_timesteps      | 14848        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 6.210094e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.94        |\n",
      "|    explained_variance   | 0.00577      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0811       |\n",
      "|    n_updates            | 280          |\n",
      "|    policy_gradient_loss | -0.000602    |\n",
      "|    value_loss           | 0.179        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 30            |\n",
      "|    time_elapsed         | 1225          |\n",
      "|    total_timesteps      | 15360         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.9693823e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | 0.00632       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0614        |\n",
      "|    n_updates            | 290           |\n",
      "|    policy_gradient_loss | -0.000399     |\n",
      "|    value_loss           | 0.181         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 31            |\n",
      "|    time_elapsed         | 1267          |\n",
      "|    total_timesteps      | 15872         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.2504362e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | 0.00391       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.166         |\n",
      "|    n_updates            | 300           |\n",
      "|    policy_gradient_loss | -0.00026      |\n",
      "|    value_loss           | 0.248         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 32           |\n",
      "|    time_elapsed         | 1309         |\n",
      "|    total_timesteps      | 16384        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 7.920957e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.94        |\n",
      "|    explained_variance   | -0.000856    |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.159        |\n",
      "|    n_updates            | 310          |\n",
      "|    policy_gradient_loss | -0.000849    |\n",
      "|    value_loss           | 0.235        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 33            |\n",
      "|    time_elapsed         | 1352          |\n",
      "|    total_timesteps      | 16896         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00010355236 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | 0.102         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 109           |\n",
      "|    n_updates            | 320           |\n",
      "|    policy_gradient_loss | 0.000438      |\n",
      "|    value_loss           | 303           |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 34           |\n",
      "|    time_elapsed         | 1393         |\n",
      "|    total_timesteps      | 17408        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 3.835419e-06 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.94        |\n",
      "|    explained_variance   | 0.102        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 8.42         |\n",
      "|    n_updates            | 330          |\n",
      "|    policy_gradient_loss | -8.63e-05    |\n",
      "|    value_loss           | 22.3         |\n",
      "------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 35            |\n",
      "|    time_elapsed         | 1433          |\n",
      "|    total_timesteps      | 17920         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.6355847e-05 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | 0.0797        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0664        |\n",
      "|    n_updates            | 340           |\n",
      "|    policy_gradient_loss | -5.31e-05     |\n",
      "|    value_loss           | 0.572         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 36            |\n",
      "|    time_elapsed         | 1474          |\n",
      "|    total_timesteps      | 18432         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.5877496e-05 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | -0.254        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.123         |\n",
      "|    n_updates            | 350           |\n",
      "|    policy_gradient_loss | -0.000222     |\n",
      "|    value_loss           | 0.432         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 37           |\n",
      "|    time_elapsed         | 1515         |\n",
      "|    total_timesteps      | 18944        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 1.991808e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.94        |\n",
      "|    explained_variance   | 0.0379       |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.136        |\n",
      "|    n_updates            | 360          |\n",
      "|    policy_gradient_loss | -0.000188    |\n",
      "|    value_loss           | 0.54         |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 38           |\n",
      "|    time_elapsed         | 1557         |\n",
      "|    total_timesteps      | 19456        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 1.781329e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.94        |\n",
      "|    explained_variance   | 0.00989      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0694       |\n",
      "|    n_updates            | 370          |\n",
      "|    policy_gradient_loss | -0.000199    |\n",
      "|    value_loss           | 0.289        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 39            |\n",
      "|    time_elapsed         | 1599          |\n",
      "|    total_timesteps      | 19968         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.7243321e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | 0.135         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.107         |\n",
      "|    n_updates            | 380           |\n",
      "|    policy_gradient_loss | -0.000186     |\n",
      "|    value_loss           | 0.368         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 40            |\n",
      "|    time_elapsed         | 1641          |\n",
      "|    total_timesteps      | 20480         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.8004986e-05 |\n",
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      "|    entropy_loss         | -1.94         |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0614        |\n",
      "|    n_updates            | 390           |\n",
      "|    policy_gradient_loss | -0.000183     |\n",
      "|    value_loss           | 0.158         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 41           |\n",
      "|    time_elapsed         | 1684         |\n",
      "|    total_timesteps      | 20992        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 6.583112e-05 |\n",
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      "|    entropy_loss         | -1.94        |\n",
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      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.109        |\n",
      "|    n_updates            | 400          |\n",
      "|    policy_gradient_loss | -0.000746    |\n",
      "|    value_loss           | 0.235        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 42            |\n",
      "|    time_elapsed         | 1726          |\n",
      "|    total_timesteps      | 21504         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.8963008e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | -0.00452      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.057         |\n",
      "|    n_updates            | 410           |\n",
      "|    policy_gradient_loss | -0.000209     |\n",
      "|    value_loss           | 0.16          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 43            |\n",
      "|    time_elapsed         | 1768          |\n",
      "|    total_timesteps      | 22016         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.2766606e-05 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.94         |\n",
      "|    explained_variance   | 0.00805       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0626        |\n",
      "|    n_updates            | 420           |\n",
      "|    policy_gradient_loss | -0.000261     |\n",
      "|    value_loss           | 0.185         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 44           |\n",
      "|    time_elapsed         | 1810         |\n",
      "|    total_timesteps      | 22528        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 6.722158e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.94        |\n",
      "|    explained_variance   | -0.0288      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.051        |\n",
      "|    n_updates            | 430          |\n",
      "|    policy_gradient_loss | -0.000712    |\n",
      "|    value_loss           | 0.117        |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 45           |\n",
      "|    time_elapsed         | 1852         |\n",
      "|    total_timesteps      | 23040        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 5.105522e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.93        |\n",
      "|    explained_variance   | -0.00666     |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0537       |\n",
      "|    n_updates            | 440          |\n",
      "|    policy_gradient_loss | -0.000442    |\n",
      "|    value_loss           | 0.117        |\n",
      "------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 46           |\n",
      "|    time_elapsed         | 1894         |\n",
      "|    total_timesteps      | 23552        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 6.681564e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.93        |\n",
      "|    explained_variance   | -0.00293     |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0606       |\n",
      "|    n_updates            | 450          |\n",
      "|    policy_gradient_loss | -0.000737    |\n",
      "|    value_loss           | 0.13         |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 47            |\n",
      "|    time_elapsed         | 1935          |\n",
      "|    total_timesteps      | 24064         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00018384983 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.93         |\n",
      "|    explained_variance   | 0.00859       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.111         |\n",
      "|    n_updates            | 460           |\n",
      "|    policy_gradient_loss | -0.00126      |\n",
      "|    value_loss           | 0.166         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 48            |\n",
      "|    time_elapsed         | 1977          |\n",
      "|    total_timesteps      | 24576         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.0184903e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.93         |\n",
      "|    explained_variance   | 0.00731       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 4.93          |\n",
      "|    n_updates            | 470           |\n",
      "|    policy_gradient_loss | -0.000298     |\n",
      "|    value_loss           | 10.2          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 49            |\n",
      "|    time_elapsed         | 2018          |\n",
      "|    total_timesteps      | 25088         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00014711102 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.93         |\n",
      "|    explained_variance   | 0.0359        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 192           |\n",
      "|    n_updates            | 480           |\n",
      "|    policy_gradient_loss | -0.000319     |\n",
      "|    value_loss           | 490           |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 50            |\n",
      "|    time_elapsed         | 2059          |\n",
      "|    total_timesteps      | 25600         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.4080935e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.93         |\n",
      "|    explained_variance   | 0.116         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.274         |\n",
      "|    n_updates            | 490           |\n",
      "|    policy_gradient_loss | -0.000184     |\n",
      "|    value_loss           | 1.06          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 51            |\n",
      "|    time_elapsed         | 2099          |\n",
      "|    total_timesteps      | 26112         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.7656282e-05 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.93         |\n",
      "|    explained_variance   | 0.34          |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0528        |\n",
      "|    n_updates            | 500           |\n",
      "|    policy_gradient_loss | -0.000229     |\n",
      "|    value_loss           | 0.346         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 52           |\n",
      "|    time_elapsed         | 2140         |\n",
      "|    total_timesteps      | 26624        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 6.170559e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.93        |\n",
      "|    explained_variance   | 0.0334       |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.103        |\n",
      "|    n_updates            | 510          |\n",
      "|    policy_gradient_loss | -0.000401    |\n",
      "|    value_loss           | 0.303        |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 53           |\n",
      "|    time_elapsed         | 2181         |\n",
      "|    total_timesteps      | 27136        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 8.158642e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.93        |\n",
      "|    explained_variance   | 0.00653      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0758       |\n",
      "|    n_updates            | 520          |\n",
      "|    policy_gradient_loss | -0.0006      |\n",
      "|    value_loss           | 0.236        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 54            |\n",
      "|    time_elapsed         | 2221          |\n",
      "|    total_timesteps      | 27648         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00014260586 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.92         |\n",
      "|    explained_variance   | 0.07          |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.107         |\n",
      "|    n_updates            | 530           |\n",
      "|    policy_gradient_loss | -0.000893     |\n",
      "|    value_loss           | 0.257         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 55           |\n",
      "|    time_elapsed         | 2262         |\n",
      "|    total_timesteps      | 28160        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 8.463778e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.92        |\n",
      "|    explained_variance   | 0.127        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0566       |\n",
      "|    n_updates            | 540          |\n",
      "|    policy_gradient_loss | -0.000761    |\n",
      "|    value_loss           | 0.192        |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 56           |\n",
      "|    time_elapsed         | 2303         |\n",
      "|    total_timesteps      | 28672        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 3.074319e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.92        |\n",
      "|    explained_variance   | -0.118       |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.062        |\n",
      "|    n_updates            | 550          |\n",
      "|    policy_gradient_loss | -0.000286    |\n",
      "|    value_loss           | 0.18         |\n",
      "------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 57           |\n",
      "|    time_elapsed         | 2344         |\n",
      "|    total_timesteps      | 29184        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 4.205585e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.92        |\n",
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      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0612       |\n",
      "|    n_updates            | 560          |\n",
      "|    policy_gradient_loss | -0.000374    |\n",
      "|    value_loss           | 0.131        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 58            |\n",
      "|    time_elapsed         | 2387          |\n",
      "|    total_timesteps      | 29696         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.0968146e-05 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.92         |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.043         |\n",
      "|    n_updates            | 570           |\n",
      "|    policy_gradient_loss | -0.000125     |\n",
      "|    value_loss           | 0.111         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 59            |\n",
      "|    time_elapsed         | 2430          |\n",
      "|    total_timesteps      | 30208         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00015285402 |\n",
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      "|    entropy_loss         | -1.92         |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.108         |\n",
      "|    n_updates            | 580           |\n",
      "|    policy_gradient_loss | -0.00114      |\n",
      "|    value_loss           | 0.167         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 60            |\n",
      "|    time_elapsed         | 2471          |\n",
      "|    total_timesteps      | 30720         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.3047556e-05 |\n",
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      "|    entropy_loss         | -1.92         |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0981        |\n",
      "|    n_updates            | 590           |\n",
      "|    policy_gradient_loss | -0.00024      |\n",
      "|    value_loss           | 0.165         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 61            |\n",
      "|    time_elapsed         | 2513          |\n",
      "|    total_timesteps      | 31232         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00018868095 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.92         |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0937        |\n",
      "|    n_updates            | 600           |\n",
      "|    policy_gradient_loss | -0.000857     |\n",
      "|    value_loss           | 0.194         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 62            |\n",
      "|    time_elapsed         | 2554          |\n",
      "|    total_timesteps      | 31744         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 4.6035042e-05 |\n",
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      "|    entropy_loss         | -1.92         |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.102         |\n",
      "|    n_updates            | 610           |\n",
      "|    policy_gradient_loss | -0.0002       |\n",
      "|    value_loss           | 0.182         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 63            |\n",
      "|    time_elapsed         | 2595          |\n",
      "|    total_timesteps      | 32256         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 4.3555745e-05 |\n",
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      "|    entropy_loss         | -1.91         |\n",
      "|    explained_variance   | -0.00449      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0704        |\n",
      "|    n_updates            | 620           |\n",
      "|    policy_gradient_loss | -0.000253     |\n",
      "|    value_loss           | 0.184         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 64           |\n",
      "|    time_elapsed         | 2637         |\n",
      "|    total_timesteps      | 32768        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0003685396 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.91        |\n",
      "|    explained_variance   | 0.1          |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 107          |\n",
      "|    n_updates            | 630          |\n",
      "|    policy_gradient_loss | -0.000122    |\n",
      "|    value_loss           | 360          |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 65            |\n",
      "|    time_elapsed         | 2678          |\n",
      "|    total_timesteps      | 33280         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00031270366 |\n",
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      "|    entropy_loss         | -1.9          |\n",
      "|    explained_variance   | 0.167         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 58            |\n",
      "|    n_updates            | 640           |\n",
      "|    policy_gradient_loss | 0.000458      |\n",
      "|    value_loss           | 96.1          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 66           |\n",
      "|    time_elapsed         | 2720         |\n",
      "|    total_timesteps      | 33792        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 9.151979e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.9         |\n",
      "|    explained_variance   | -0.117       |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.154        |\n",
      "|    n_updates            | 650          |\n",
      "|    policy_gradient_loss | -0.000148    |\n",
      "|    value_loss           | 1.08         |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 67           |\n",
      "|    time_elapsed         | 2762         |\n",
      "|    total_timesteps      | 34304        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 1.890643e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.9         |\n",
      "|    explained_variance   | -0.00557     |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.148        |\n",
      "|    n_updates            | 660          |\n",
      "|    policy_gradient_loss | 0.00027      |\n",
      "|    value_loss           | 0.856        |\n",
      "------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 68           |\n",
      "|    time_elapsed         | 2804         |\n",
      "|    total_timesteps      | 34816        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 6.219186e-05 |\n",
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      "|    entropy_loss         | -1.9         |\n",
      "|    explained_variance   | 0.00117      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.2          |\n",
      "|    n_updates            | 670          |\n",
      "|    policy_gradient_loss | -0.000307    |\n",
      "|    value_loss           | 0.555        |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 69           |\n",
      "|    time_elapsed         | 2846         |\n",
      "|    total_timesteps      | 35328        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 5.159527e-06 |\n",
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      "|    entropy_loss         | -1.9         |\n",
      "|    explained_variance   | 0.249        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.158        |\n",
      "|    n_updates            | 680          |\n",
      "|    policy_gradient_loss | 0.000162     |\n",
      "|    value_loss           | 0.524        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 70            |\n",
      "|    time_elapsed         | 2888          |\n",
      "|    total_timesteps      | 35840         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 4.8471382e-05 |\n",
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      "|    entropy_loss         | -1.9          |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.144         |\n",
      "|    n_updates            | 690           |\n",
      "|    policy_gradient_loss | -0.000188     |\n",
      "|    value_loss           | 0.486         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 71            |\n",
      "|    time_elapsed         | 2930          |\n",
      "|    total_timesteps      | 36352         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.5290723e-05 |\n",
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      "|    entropy_loss         | -1.9          |\n",
      "|    explained_variance   | 0.221         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0965        |\n",
      "|    n_updates            | 700           |\n",
      "|    policy_gradient_loss | -0.000192     |\n",
      "|    value_loss           | 0.377         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 72            |\n",
      "|    time_elapsed         | 2972          |\n",
      "|    total_timesteps      | 36864         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00012104842 |\n",
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      "|    entropy_loss         | -1.9          |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.139         |\n",
      "|    n_updates            | 710           |\n",
      "|    policy_gradient_loss | -0.000881     |\n",
      "|    value_loss           | 0.384         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 73           |\n",
      "|    time_elapsed         | 3013         |\n",
      "|    total_timesteps      | 37376        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 6.950507e-05 |\n",
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      "|    entropy_loss         | -1.9         |\n",
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      "|    learning_rate        | 1e-06        |\n",
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      "|    n_updates            | 720          |\n",
      "|    policy_gradient_loss | -0.000368    |\n",
      "|    value_loss           | 0.29         |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 74           |\n",
      "|    time_elapsed         | 3055         |\n",
      "|    total_timesteps      | 37888        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 5.573081e-05 |\n",
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      "|    entropy_loss         | -1.9         |\n",
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      "|    learning_rate        | 1e-06        |\n",
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      "|    n_updates            | 730          |\n",
      "|    policy_gradient_loss | -0.00037     |\n",
      "|    value_loss           | 0.266        |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 75           |\n",
      "|    time_elapsed         | 3097         |\n",
      "|    total_timesteps      | 38400        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0001605642 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.9         |\n",
      "|    explained_variance   | -0.0335      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0408       |\n",
      "|    n_updates            | 740          |\n",
      "|    policy_gradient_loss | -0.000725    |\n",
      "|    value_loss           | 0.117        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 76            |\n",
      "|    time_elapsed         | 3139          |\n",
      "|    total_timesteps      | 38912         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00011019211 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.9          |\n",
      "|    explained_variance   | -0.0152       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0708        |\n",
      "|    n_updates            | 750           |\n",
      "|    policy_gradient_loss | -0.000577     |\n",
      "|    value_loss           | 0.13          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 77           |\n",
      "|    time_elapsed         | 3181         |\n",
      "|    total_timesteps      | 39424        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 8.609216e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.91        |\n",
      "|    explained_variance   | 0.205        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0405       |\n",
      "|    n_updates            | 760          |\n",
      "|    policy_gradient_loss | -0.000587    |\n",
      "|    value_loss           | 0.107        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 78            |\n",
      "|    time_elapsed         | 3223          |\n",
      "|    total_timesteps      | 39936         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00016480172 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.91         |\n",
      "|    explained_variance   | 0.106         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0472        |\n",
      "|    n_updates            | 770           |\n",
      "|    policy_gradient_loss | -0.000902     |\n",
      "|    value_loss           | 0.132         |\n",
      "-------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 79            |\n",
      "|    time_elapsed         | 3266          |\n",
      "|    total_timesteps      | 40448         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00023095077 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.9          |\n",
      "|    explained_variance   | 0.191         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0579        |\n",
      "|    n_updates            | 780           |\n",
      "|    policy_gradient_loss | -0.00107      |\n",
      "|    value_loss           | 0.132         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 80           |\n",
      "|    time_elapsed         | 3308         |\n",
      "|    total_timesteps      | 40960        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0008784523 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.89        |\n",
      "|    explained_variance   | 0.133        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 145          |\n",
      "|    n_updates            | 790          |\n",
      "|    policy_gradient_loss | 1.18e-06     |\n",
      "|    value_loss           | 448          |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 81            |\n",
      "|    time_elapsed         | 3350          |\n",
      "|    total_timesteps      | 41472         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 9.9674915e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.88         |\n",
      "|    explained_variance   | 0.011         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0517        |\n",
      "|    n_updates            | 800           |\n",
      "|    policy_gradient_loss | -0.000349     |\n",
      "|    value_loss           | 1.25          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 82           |\n",
      "|    time_elapsed         | 3392         |\n",
      "|    total_timesteps      | 41984        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 9.992835e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.89        |\n",
      "|    explained_variance   | -0.00663     |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0526       |\n",
      "|    n_updates            | 810          |\n",
      "|    policy_gradient_loss | -0.000352    |\n",
      "|    value_loss           | 0.438        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 83            |\n",
      "|    time_elapsed         | 3434          |\n",
      "|    total_timesteps      | 42496         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00022892875 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.89         |\n",
      "|    explained_variance   | -0.0138       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.047         |\n",
      "|    n_updates            | 820           |\n",
      "|    policy_gradient_loss | -0.00145      |\n",
      "|    value_loss           | 0.371         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 84           |\n",
      "|    time_elapsed         | 3475         |\n",
      "|    total_timesteps      | 43008        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 7.514702e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.9         |\n",
      "|    explained_variance   | 0.0344       |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0932       |\n",
      "|    n_updates            | 830          |\n",
      "|    policy_gradient_loss | -0.000376    |\n",
      "|    value_loss           | 0.301        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 85            |\n",
      "|    time_elapsed         | 3517          |\n",
      "|    total_timesteps      | 43520         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 5.2896445e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.9          |\n",
      "|    explained_variance   | 0.0758        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0617        |\n",
      "|    n_updates            | 840           |\n",
      "|    policy_gradient_loss | -0.000259     |\n",
      "|    value_loss           | 0.194         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 86            |\n",
      "|    time_elapsed         | 3559          |\n",
      "|    total_timesteps      | 44032         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00029565243 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.9          |\n",
      "|    explained_variance   | 0.0653        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0672        |\n",
      "|    n_updates            | 850           |\n",
      "|    policy_gradient_loss | -0.00136      |\n",
      "|    value_loss           | 0.186         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 87            |\n",
      "|    time_elapsed         | 3601          |\n",
      "|    total_timesteps      | 44544         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00013438682 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.9          |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.062         |\n",
      "|    n_updates            | 860           |\n",
      "|    policy_gradient_loss | -0.0008       |\n",
      "|    value_loss           | 0.157         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 88           |\n",
      "|    time_elapsed         | 3642         |\n",
      "|    total_timesteps      | 45056        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 7.899932e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.9         |\n",
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      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0495       |\n",
      "|    n_updates            | 870          |\n",
      "|    policy_gradient_loss | -0.000551    |\n",
      "|    value_loss           | 0.158        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 89            |\n",
      "|    time_elapsed         | 3684          |\n",
      "|    total_timesteps      | 45568         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00015839946 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.9          |\n",
      "|    explained_variance   | 0.192         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0716        |\n",
      "|    n_updates            | 880           |\n",
      "|    policy_gradient_loss | -0.0011       |\n",
      "|    value_loss           | 0.166         |\n",
      "-------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 90            |\n",
      "|    time_elapsed         | 3725          |\n",
      "|    total_timesteps      | 46080         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00011557422 |\n",
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      "|    entropy_loss         | -1.9          |\n",
      "|    explained_variance   | 0.0869        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0702        |\n",
      "|    n_updates            | 890           |\n",
      "|    policy_gradient_loss | -0.000903     |\n",
      "|    value_loss           | 0.141         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 91            |\n",
      "|    time_elapsed         | 3767          |\n",
      "|    total_timesteps      | 46592         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00033916696 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.89         |\n",
      "|    explained_variance   | 0.0258        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.059         |\n",
      "|    n_updates            | 900           |\n",
      "|    policy_gradient_loss | -0.00181      |\n",
      "|    value_loss           | 0.14          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 92           |\n",
      "|    time_elapsed         | 3809         |\n",
      "|    total_timesteps      | 47104        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 4.618184e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.89        |\n",
      "|    explained_variance   | -0.0109      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0633       |\n",
      "|    n_updates            | 910          |\n",
      "|    policy_gradient_loss | -0.000169    |\n",
      "|    value_loss           | 0.118        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 93            |\n",
      "|    time_elapsed         | 3851          |\n",
      "|    total_timesteps      | 47616         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 5.8326637e-05 |\n",
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      "|    entropy_loss         | -1.89         |\n",
      "|    explained_variance   | 0.0889        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0452        |\n",
      "|    n_updates            | 920           |\n",
      "|    policy_gradient_loss | -0.000515     |\n",
      "|    value_loss           | 0.115         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 94            |\n",
      "|    time_elapsed         | 3893          |\n",
      "|    total_timesteps      | 48128         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00016686879 |\n",
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      "|    entropy_loss         | -1.89         |\n",
      "|    explained_variance   | 0.00114       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0512        |\n",
      "|    n_updates            | 930           |\n",
      "|    policy_gradient_loss | -0.000883     |\n",
      "|    value_loss           | 0.114         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 95            |\n",
      "|    time_elapsed         | 3935          |\n",
      "|    total_timesteps      | 48640         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.5886187e-06 |\n",
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      "|    entropy_loss         | -1.9          |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 2.72          |\n",
      "|    n_updates            | 940           |\n",
      "|    policy_gradient_loss | 0.000197      |\n",
      "|    value_loss           | 10.3          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 96           |\n",
      "|    time_elapsed         | 3977         |\n",
      "|    total_timesteps      | 49152        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0006195295 |\n",
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      "|    entropy_loss         | -1.89        |\n",
      "|    explained_variance   | 0.058        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 123          |\n",
      "|    n_updates            | 950          |\n",
      "|    policy_gradient_loss | -0.000338    |\n",
      "|    value_loss           | 374          |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 97           |\n",
      "|    time_elapsed         | 4019         |\n",
      "|    total_timesteps      | 49664        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 8.178968e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.89        |\n",
      "|    explained_variance   | -0.0242      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0556       |\n",
      "|    n_updates            | 960          |\n",
      "|    policy_gradient_loss | -0.000102    |\n",
      "|    value_loss           | 1.19         |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 98           |\n",
      "|    time_elapsed         | 4062         |\n",
      "|    total_timesteps      | 50176        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 9.635079e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.89        |\n",
      "|    explained_variance   | 0.043        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0638       |\n",
      "|    n_updates            | 970          |\n",
      "|    policy_gradient_loss | -0.000357    |\n",
      "|    value_loss           | 0.389        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 99            |\n",
      "|    time_elapsed         | 4104          |\n",
      "|    total_timesteps      | 50688         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.7743517e-05 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.89         |\n",
      "|    explained_variance   | 0.131         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0651        |\n",
      "|    n_updates            | 980           |\n",
      "|    policy_gradient_loss | -3.53e-05     |\n",
      "|    value_loss           | 0.287         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 100          |\n",
      "|    time_elapsed         | 4146         |\n",
      "|    total_timesteps      | 51200        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 3.202283e-05 |\n",
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      "|    entropy_loss         | -1.89        |\n",
      "|    explained_variance   | -0.0121      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0476       |\n",
      "|    n_updates            | 990          |\n",
      "|    policy_gradient_loss | -0.000121    |\n",
      "|    value_loss           | 0.177        |\n",
      "------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 101           |\n",
      "|    time_elapsed         | 4188          |\n",
      "|    total_timesteps      | 51712         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 5.1365234e-05 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.89         |\n",
      "|    explained_variance   | 0.0958        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0453        |\n",
      "|    n_updates            | 1000          |\n",
      "|    policy_gradient_loss | -0.000387     |\n",
      "|    value_loss           | 0.213         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 102           |\n",
      "|    time_elapsed         | 4230          |\n",
      "|    total_timesteps      | 52224         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.7060665e-05 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.89         |\n",
      "|    explained_variance   | 0.000889      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0583        |\n",
      "|    n_updates            | 1010          |\n",
      "|    policy_gradient_loss | -5.36e-05     |\n",
      "|    value_loss           | 0.132         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 103           |\n",
      "|    time_elapsed         | 4272          |\n",
      "|    total_timesteps      | 52736         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 6.0526538e-05 |\n",
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      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.9          |\n",
      "|    explained_variance   | 0.00357       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0692        |\n",
      "|    n_updates            | 1020          |\n",
      "|    policy_gradient_loss | -0.000412     |\n",
      "|    value_loss           | 0.134         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 104          |\n",
      "|    time_elapsed         | 4314         |\n",
      "|    total_timesteps      | 53248        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 7.442944e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.89        |\n",
      "|    explained_variance   | -0.00206     |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0582       |\n",
      "|    n_updates            | 1030         |\n",
      "|    policy_gradient_loss | -0.000478    |\n",
      "|    value_loss           | 0.133        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 105           |\n",
      "|    time_elapsed         | 4356          |\n",
      "|    total_timesteps      | 53760         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00012411259 |\n",
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      "|    entropy_loss         | -1.89         |\n",
      "|    explained_variance   | 0.504         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0988        |\n",
      "|    n_updates            | 1040          |\n",
      "|    policy_gradient_loss | -0.000813     |\n",
      "|    value_loss           | 0.142         |\n",
      "-------------------------------------------\n",
      "--------------------------------------------\n",
      "| time/                   |                |\n",
      "|    fps                  | 12             |\n",
      "|    iterations           | 106            |\n",
      "|    time_elapsed         | 4397           |\n",
      "|    total_timesteps      | 54272          |\n",
      "| train/                  |                |\n",
      "|    approx_kl            | 0.000108138425 |\n",
      "|    clip_fraction        | 0              |\n",
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      "|    entropy_loss         | -1.89          |\n",
      "|    explained_variance   | -0.00908       |\n",
      "|    learning_rate        | 1e-06          |\n",
      "|    loss                 | 0.0683         |\n",
      "|    n_updates            | 1050           |\n",
      "|    policy_gradient_loss | -0.000622      |\n",
      "|    value_loss           | 0.129          |\n",
      "--------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 107           |\n",
      "|    time_elapsed         | 4439          |\n",
      "|    total_timesteps      | 54784         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00041910843 |\n",
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      "|    entropy_loss         | -1.89         |\n",
      "|    explained_variance   | 0.132         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0638        |\n",
      "|    n_updates            | 1060          |\n",
      "|    policy_gradient_loss | -0.00185      |\n",
      "|    value_loss           | 0.145         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 108           |\n",
      "|    time_elapsed         | 4481          |\n",
      "|    total_timesteps      | 55296         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.0186027e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.88         |\n",
      "|    explained_variance   | 0.0322        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0914        |\n",
      "|    n_updates            | 1070          |\n",
      "|    policy_gradient_loss | -0.00017      |\n",
      "|    value_loss           | 0.135         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 109          |\n",
      "|    time_elapsed         | 4523         |\n",
      "|    total_timesteps      | 55808        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0002167488 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.88        |\n",
      "|    explained_variance   | -0.000298    |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0386       |\n",
      "|    n_updates            | 1080         |\n",
      "|    policy_gradient_loss | -0.00139     |\n",
      "|    value_loss           | 0.0976       |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 110          |\n",
      "|    time_elapsed         | 4565         |\n",
      "|    total_timesteps      | 56320        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 4.309381e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.88        |\n",
      "|    explained_variance   | 0.012        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0479       |\n",
      "|    n_updates            | 1090         |\n",
      "|    policy_gradient_loss | -0.000209    |\n",
      "|    value_loss           | 0.0948       |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 111          |\n",
      "|    time_elapsed         | 4606         |\n",
      "|    total_timesteps      | 56832        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0007202084 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.87        |\n",
      "|    explained_variance   | 0.174        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 70.8         |\n",
      "|    n_updates            | 1100         |\n",
      "|    policy_gradient_loss | 0.000407     |\n",
      "|    value_loss           | 197          |\n",
      "------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 112          |\n",
      "|    time_elapsed         | 4649         |\n",
      "|    total_timesteps      | 57344        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0010185962 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.86        |\n",
      "|    explained_variance   | 0.226        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 79.2         |\n",
      "|    n_updates            | 1110         |\n",
      "|    policy_gradient_loss | 0.0014       |\n",
      "|    value_loss           | 185          |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 113           |\n",
      "|    time_elapsed         | 4691          |\n",
      "|    total_timesteps      | 57856         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.3829827e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.85         |\n",
      "|    explained_variance   | -0.00883      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.085         |\n",
      "|    n_updates            | 1120          |\n",
      "|    policy_gradient_loss | 0.000276      |\n",
      "|    value_loss           | 1.03          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 114          |\n",
      "|    time_elapsed         | 4733         |\n",
      "|    total_timesteps      | 58368        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 9.416242e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.86        |\n",
      "|    explained_variance   | 0.0114       |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.133        |\n",
      "|    n_updates            | 1130         |\n",
      "|    policy_gradient_loss | -0.000468    |\n",
      "|    value_loss           | 0.944        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 115           |\n",
      "|    time_elapsed         | 4775          |\n",
      "|    total_timesteps      | 58880         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00012746197 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.86         |\n",
      "|    explained_variance   | 0.0218        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.116         |\n",
      "|    n_updates            | 1140          |\n",
      "|    policy_gradient_loss | -0.000518     |\n",
      "|    value_loss           | 0.609         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 116          |\n",
      "|    time_elapsed         | 4817         |\n",
      "|    total_timesteps      | 59392        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 6.091944e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.86        |\n",
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      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.127        |\n",
      "|    n_updates            | 1150         |\n",
      "|    policy_gradient_loss | -0.000122    |\n",
      "|    value_loss           | 0.455        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 117           |\n",
      "|    time_elapsed         | 4858          |\n",
      "|    total_timesteps      | 59904         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00010348763 |\n",
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      "|    entropy_loss         | -1.86         |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.143         |\n",
      "|    n_updates            | 1160          |\n",
      "|    policy_gradient_loss | -0.000698     |\n",
      "|    value_loss           | 0.397         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 118           |\n",
      "|    time_elapsed         | 4900          |\n",
      "|    total_timesteps      | 60416         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 5.0334027e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.86         |\n",
      "|    explained_variance   | 0.169         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.125         |\n",
      "|    n_updates            | 1170          |\n",
      "|    policy_gradient_loss | -0.000255     |\n",
      "|    value_loss           | 0.332         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 119           |\n",
      "|    time_elapsed         | 4943          |\n",
      "|    total_timesteps      | 60928         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00016150868 |\n",
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      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.86         |\n",
      "|    explained_variance   | -0.0014       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.149         |\n",
      "|    n_updates            | 1180          |\n",
      "|    policy_gradient_loss | -0.000954     |\n",
      "|    value_loss           | 0.347         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 120           |\n",
      "|    time_elapsed         | 4984          |\n",
      "|    total_timesteps      | 61440         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00014481845 |\n",
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      "|    entropy_loss         | -1.86         |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.16          |\n",
      "|    n_updates            | 1190          |\n",
      "|    policy_gradient_loss | -0.00082      |\n",
      "|    value_loss           | 0.319         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 121          |\n",
      "|    time_elapsed         | 5026         |\n",
      "|    total_timesteps      | 61952        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 9.388605e-05 |\n",
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      "|    entropy_loss         | -1.86        |\n",
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      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.22         |\n",
      "|    n_updates            | 1200         |\n",
      "|    policy_gradient_loss | -0.000503    |\n",
      "|    value_loss           | 0.311        |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 122          |\n",
      "|    time_elapsed         | 5067         |\n",
      "|    total_timesteps      | 62464        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 7.499533e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.86        |\n",
      "|    explained_variance   | -0.0101      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0589       |\n",
      "|    n_updates            | 1210         |\n",
      "|    policy_gradient_loss | -0.000444    |\n",
      "|    value_loss           | 0.155        |\n",
      "------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 123           |\n",
      "|    time_elapsed         | 5109          |\n",
      "|    total_timesteps      | 62976         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.6163605e-05 |\n",
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      "|    entropy_loss         | -1.87         |\n",
      "|    explained_variance   | 0.0257        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0468        |\n",
      "|    n_updates            | 1220          |\n",
      "|    policy_gradient_loss | -0.000257     |\n",
      "|    value_loss           | 0.113         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 124           |\n",
      "|    time_elapsed         | 5151          |\n",
      "|    total_timesteps      | 63488         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00013010972 |\n",
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      "|    entropy_loss         | -1.86         |\n",
      "|    explained_variance   | -0.0306       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.041         |\n",
      "|    n_updates            | 1230          |\n",
      "|    policy_gradient_loss | -0.000859     |\n",
      "|    value_loss           | 0.105         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 125          |\n",
      "|    time_elapsed         | 5193         |\n",
      "|    total_timesteps      | 64000        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 5.649624e-05 |\n",
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      "|    entropy_loss         | -1.86        |\n",
      "|    explained_variance   | 0.199        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0491       |\n",
      "|    n_updates            | 1240         |\n",
      "|    policy_gradient_loss | -0.000298    |\n",
      "|    value_loss           | 0.152        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 126           |\n",
      "|    time_elapsed         | 5235          |\n",
      "|    total_timesteps      | 64512         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 4.4743996e-05 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.86         |\n",
      "|    explained_variance   | 0.000675      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0581        |\n",
      "|    n_updates            | 1250          |\n",
      "|    policy_gradient_loss | -0.000183     |\n",
      "|    value_loss           | 0.114         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 127          |\n",
      "|    time_elapsed         | 5276         |\n",
      "|    total_timesteps      | 65024        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0013513819 |\n",
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      "|    entropy_loss         | -1.85        |\n",
      "|    explained_variance   | 0.224        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 91.5         |\n",
      "|    n_updates            | 1260         |\n",
      "|    policy_gradient_loss | 0.00126      |\n",
      "|    value_loss           | 324          |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 128           |\n",
      "|    time_elapsed         | 5319          |\n",
      "|    total_timesteps      | 65536         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.8454739e-05 |\n",
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      "|    learning_rate        | 1e-06         |\n",
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      "|    n_updates            | 1270          |\n",
      "|    policy_gradient_loss | -1.64e-05     |\n",
      "|    value_loss           | 8.75          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 129           |\n",
      "|    time_elapsed         | 5361          |\n",
      "|    total_timesteps      | 66048         |\n",
      "| train/                  |               |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0754        |\n",
      "|    n_updates            | 1280          |\n",
      "|    policy_gradient_loss | -0.000975     |\n",
      "|    value_loss           | 0.476         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 130          |\n",
      "|    time_elapsed         | 5403         |\n",
      "|    total_timesteps      | 66560        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 4.007644e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    explained_variance   | -0.0297      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0552       |\n",
      "|    n_updates            | 1290         |\n",
      "|    policy_gradient_loss | 0.000132     |\n",
      "|    value_loss           | 0.405        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 131           |\n",
      "|    time_elapsed         | 5445          |\n",
      "|    total_timesteps      | 67072         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.6559533e-05 |\n",
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      "|    entropy_loss         | -1.85         |\n",
      "|    explained_variance   | -0.0215       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0605        |\n",
      "|    n_updates            | 1300          |\n",
      "|    policy_gradient_loss | -0.000137     |\n",
      "|    value_loss           | 0.29          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 132          |\n",
      "|    time_elapsed         | 5487         |\n",
      "|    total_timesteps      | 67584        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 9.479339e-05 |\n",
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      "|    entropy_loss         | -1.85        |\n",
      "|    explained_variance   | -0.0413      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0996       |\n",
      "|    n_updates            | 1310         |\n",
      "|    policy_gradient_loss | -0.000626    |\n",
      "|    value_loss           | 0.228        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 133           |\n",
      "|    time_elapsed         | 5529          |\n",
      "|    total_timesteps      | 68096         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.0531978e-05 |\n",
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      "|    entropy_loss         | -1.85         |\n",
      "|    explained_variance   | -0.0179       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0852        |\n",
      "|    n_updates            | 1320          |\n",
      "|    policy_gradient_loss | 4.67e-05      |\n",
      "|    value_loss           | 0.201         |\n",
      "-------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 134          |\n",
      "|    time_elapsed         | 5571         |\n",
      "|    total_timesteps      | 68608        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 7.988722e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.85        |\n",
      "|    explained_variance   | -0.0255      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0525       |\n",
      "|    n_updates            | 1330         |\n",
      "|    policy_gradient_loss | -0.000358    |\n",
      "|    value_loss           | 0.155        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 135           |\n",
      "|    time_elapsed         | 5613          |\n",
      "|    total_timesteps      | 69120         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 9.0383925e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.85         |\n",
      "|    explained_variance   | -0.0133       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0509        |\n",
      "|    n_updates            | 1340          |\n",
      "|    policy_gradient_loss | -0.000411     |\n",
      "|    value_loss           | 0.128         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 136          |\n",
      "|    time_elapsed         | 5654         |\n",
      "|    total_timesteps      | 69632        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 5.265104e-05 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.86        |\n",
      "|    explained_variance   | -0.0864      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0721       |\n",
      "|    n_updates            | 1350         |\n",
      "|    policy_gradient_loss | -0.000233    |\n",
      "|    value_loss           | 0.195        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 137           |\n",
      "|    time_elapsed         | 5697          |\n",
      "|    total_timesteps      | 70144         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00031185616 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.85         |\n",
      "|    explained_variance   | -0.02         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0479        |\n",
      "|    n_updates            | 1360          |\n",
      "|    policy_gradient_loss | -0.00159      |\n",
      "|    value_loss           | 0.14          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 138          |\n",
      "|    time_elapsed         | 5739         |\n",
      "|    total_timesteps      | 70656        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0015150894 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.83        |\n",
      "|    explained_variance   | 0.22         |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 101          |\n",
      "|    n_updates            | 1370         |\n",
      "|    policy_gradient_loss | 0.00261      |\n",
      "|    value_loss           | 268          |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 139          |\n",
      "|    time_elapsed         | 5781         |\n",
      "|    total_timesteps      | 71168        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0024212827 |\n",
      "|    clip_fraction        | 0.00371      |\n",
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      "|    entropy_loss         | -1.8         |\n",
      "|    explained_variance   | 0.283        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 160          |\n",
      "|    n_updates            | 1380         |\n",
      "|    policy_gradient_loss | 0.000159     |\n",
      "|    value_loss           | 459          |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 140           |\n",
      "|    time_elapsed         | 5824          |\n",
      "|    total_timesteps      | 71680         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00020098616 |\n",
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      "|    entropy_loss         | -1.79         |\n",
      "|    explained_variance   | -0.0353       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.231         |\n",
      "|    n_updates            | 1390          |\n",
      "|    policy_gradient_loss | -0.000559     |\n",
      "|    value_loss           | 3.79          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 141          |\n",
      "|    time_elapsed         | 5866         |\n",
      "|    total_timesteps      | 72192        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0001736983 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.8         |\n",
      "|    explained_variance   | -0.0411      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0655       |\n",
      "|    n_updates            | 1400         |\n",
      "|    policy_gradient_loss | 0.000209     |\n",
      "|    value_loss           | 1.88         |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 142           |\n",
      "|    time_elapsed         | 5907          |\n",
      "|    total_timesteps      | 72704         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00031178736 |\n",
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      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.81         |\n",
      "|    explained_variance   | 0.642         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0683        |\n",
      "|    n_updates            | 1410          |\n",
      "|    policy_gradient_loss | -0.00253      |\n",
      "|    value_loss           | 1.19          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 143           |\n",
      "|    time_elapsed         | 5949          |\n",
      "|    total_timesteps      | 73216         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 2.9282295e-05 |\n",
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      "|    entropy_loss         | -1.81         |\n",
      "|    explained_variance   | 0.0847        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 12.1          |\n",
      "|    n_updates            | 1420          |\n",
      "|    policy_gradient_loss | -0.000263     |\n",
      "|    value_loss           | 26.6          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 144          |\n",
      "|    time_elapsed         | 5991         |\n",
      "|    total_timesteps      | 73728        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 4.287029e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.81        |\n",
      "|    explained_variance   | 0.0507       |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 13.8         |\n",
      "|    n_updates            | 1430         |\n",
      "|    policy_gradient_loss | -0.000528    |\n",
      "|    value_loss           | 30.8         |\n",
      "------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 145           |\n",
      "|    time_elapsed         | 6033          |\n",
      "|    total_timesteps      | 74240         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00015299709 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.82         |\n",
      "|    explained_variance   | 0.065         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.136         |\n",
      "|    n_updates            | 1440          |\n",
      "|    policy_gradient_loss | -0.000478     |\n",
      "|    value_loss           | 1.74          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 146           |\n",
      "|    time_elapsed         | 6075          |\n",
      "|    total_timesteps      | 74752         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00015581073 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.82         |\n",
      "|    explained_variance   | 0.0531        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0454        |\n",
      "|    n_updates            | 1450          |\n",
      "|    policy_gradient_loss | -0.000297     |\n",
      "|    value_loss           | 0.978         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 147           |\n",
      "|    time_elapsed         | 6117          |\n",
      "|    total_timesteps      | 75264         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00013859512 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.83         |\n",
      "|    explained_variance   | -0.0419       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0505        |\n",
      "|    n_updates            | 1460          |\n",
      "|    policy_gradient_loss | -0.000499     |\n",
      "|    value_loss           | 0.766         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 148          |\n",
      "|    time_elapsed         | 6159         |\n",
      "|    total_timesteps      | 75776        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0001523419 |\n",
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      "|    entropy_loss         | -1.84        |\n",
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      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.143        |\n",
      "|    n_updates            | 1470         |\n",
      "|    policy_gradient_loss | -0.000455    |\n",
      "|    value_loss           | 0.766        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 149           |\n",
      "|    time_elapsed         | 6201          |\n",
      "|    total_timesteps      | 76288         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00011460984 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.84         |\n",
      "|    explained_variance   | 0.0351        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.061         |\n",
      "|    n_updates            | 1480          |\n",
      "|    policy_gradient_loss | -0.000342     |\n",
      "|    value_loss           | 0.53          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 150           |\n",
      "|    time_elapsed         | 6242          |\n",
      "|    total_timesteps      | 76800         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00015744916 |\n",
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      "|    entropy_loss         | -1.84         |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0649        |\n",
      "|    n_updates            | 1490          |\n",
      "|    policy_gradient_loss | -0.000292     |\n",
      "|    value_loss           | 0.359         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 151           |\n",
      "|    time_elapsed         | 6285          |\n",
      "|    total_timesteps      | 77312         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 5.3031603e-05 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.83         |\n",
      "|    explained_variance   | -0.0344       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0437        |\n",
      "|    n_updates            | 1500          |\n",
      "|    policy_gradient_loss | -0.000188     |\n",
      "|    value_loss           | 0.253         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 152          |\n",
      "|    time_elapsed         | 6327         |\n",
      "|    total_timesteps      | 77824        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0002339991 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.83        |\n",
      "|    explained_variance   | -0.00921     |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0697       |\n",
      "|    n_updates            | 1510         |\n",
      "|    policy_gradient_loss | -0.00107     |\n",
      "|    value_loss           | 0.2          |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 153           |\n",
      "|    time_elapsed         | 6369          |\n",
      "|    total_timesteps      | 78336         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00031094323 |\n",
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      "|    entropy_loss         | -1.84         |\n",
      "|    explained_variance   | 0.00636       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0329        |\n",
      "|    n_updates            | 1520          |\n",
      "|    policy_gradient_loss | -0.00131      |\n",
      "|    value_loss           | 0.189         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 154           |\n",
      "|    time_elapsed         | 6411          |\n",
      "|    total_timesteps      | 78848         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00054658076 |\n",
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      "|    entropy_loss         | -1.83         |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 96.5          |\n",
      "|    n_updates            | 1530          |\n",
      "|    policy_gradient_loss | -0.000438     |\n",
      "|    value_loss           | 245           |\n",
      "-------------------------------------------\n",
      "-----------------------------------------\n",
      "| time/                   |             |\n",
      "|    fps                  | 12          |\n",
      "|    iterations           | 155         |\n",
      "|    time_elapsed         | 6453        |\n",
      "|    total_timesteps      | 79360       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.000433572 |\n",
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      "|    entropy_loss         | -1.82       |\n",
      "|    explained_variance   | 0.45        |\n",
      "|    learning_rate        | 1e-06       |\n",
      "|    loss                 | 45.3        |\n",
      "|    n_updates            | 1540        |\n",
      "|    policy_gradient_loss | -0.00054    |\n",
      "|    value_loss           | 100         |\n",
      "-----------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 156           |\n",
      "|    time_elapsed         | 6495          |\n",
      "|    total_timesteps      | 79872         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.6942423e-05 |\n",
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      "|    entropy_loss         | -1.82         |\n",
      "|    explained_variance   | -0.0168       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.117         |\n",
      "|    n_updates            | 1550          |\n",
      "|    policy_gradient_loss | -6.5e-06      |\n",
      "|    value_loss           | 0.697         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 157          |\n",
      "|    time_elapsed         | 6537         |\n",
      "|    total_timesteps      | 80384        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 6.376114e-05 |\n",
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      "|    entropy_loss         | -1.82        |\n",
      "|    explained_variance   | -0.00162     |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.139        |\n",
      "|    n_updates            | 1560         |\n",
      "|    policy_gradient_loss | -0.000214    |\n",
      "|    value_loss           | 0.494        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 158           |\n",
      "|    time_elapsed         | 6580          |\n",
      "|    total_timesteps      | 80896         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00014303823 |\n",
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      "|    entropy_loss         | -1.81         |\n",
      "|    explained_variance   | 0.331         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 25.6          |\n",
      "|    n_updates            | 1570          |\n",
      "|    policy_gradient_loss | 0.00024       |\n",
      "|    value_loss           | 62.7          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 159           |\n",
      "|    time_elapsed         | 6622          |\n",
      "|    total_timesteps      | 81408         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 6.7938585e-05 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.81         |\n",
      "|    explained_variance   | 0.233         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 1.56          |\n",
      "|    n_updates            | 1580          |\n",
      "|    policy_gradient_loss | 0.000202      |\n",
      "|    value_loss           | 4.62          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 160           |\n",
      "|    time_elapsed         | 6663          |\n",
      "|    total_timesteps      | 81920         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00017160829 |\n",
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      "|    entropy_loss         | -1.81         |\n",
      "|    explained_variance   | 0.00948       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.147         |\n",
      "|    n_updates            | 1590          |\n",
      "|    policy_gradient_loss | -0.000368     |\n",
      "|    value_loss           | 1.43          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 161          |\n",
      "|    time_elapsed         | 6705         |\n",
      "|    total_timesteps      | 82432        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 9.664684e-06 |\n",
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      "|    entropy_loss         | -1.81        |\n",
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      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.11         |\n",
      "|    n_updates            | 1600         |\n",
      "|    policy_gradient_loss | 0.000185     |\n",
      "|    value_loss           | 0.94         |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 162           |\n",
      "|    time_elapsed         | 6747          |\n",
      "|    total_timesteps      | 82944         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 8.9262845e-05 |\n",
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      "|    entropy_loss         | -1.81         |\n",
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      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.157         |\n",
      "|    n_updates            | 1610          |\n",
      "|    policy_gradient_loss | -0.000586     |\n",
      "|    value_loss           | 0.672         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 163           |\n",
      "|    time_elapsed         | 6789          |\n",
      "|    total_timesteps      | 83456         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00020587805 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.81         |\n",
      "|    explained_variance   | -0.0109       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.113         |\n",
      "|    n_updates            | 1620          |\n",
      "|    policy_gradient_loss | -0.000652     |\n",
      "|    value_loss           | 0.535         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 164           |\n",
      "|    time_elapsed         | 6831          |\n",
      "|    total_timesteps      | 83968         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00012078183 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.81         |\n",
      "|    explained_variance   | 0.0713        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.124         |\n",
      "|    n_updates            | 1630          |\n",
      "|    policy_gradient_loss | -0.000511     |\n",
      "|    value_loss           | 0.386         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 165           |\n",
      "|    time_elapsed         | 6873          |\n",
      "|    total_timesteps      | 84480         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00025412044 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.82         |\n",
      "|    explained_variance   | -0.00447      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0569        |\n",
      "|    n_updates            | 1640          |\n",
      "|    policy_gradient_loss | -0.000846     |\n",
      "|    value_loss           | 0.207         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 166           |\n",
      "|    time_elapsed         | 6914          |\n",
      "|    total_timesteps      | 84992         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00014541461 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.82         |\n",
      "|    explained_variance   | 0.000424      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.053         |\n",
      "|    n_updates            | 1650          |\n",
      "|    policy_gradient_loss | -0.000425     |\n",
      "|    value_loss           | 0.175         |\n",
      "-------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 167           |\n",
      "|    time_elapsed         | 6957          |\n",
      "|    total_timesteps      | 85504         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00014247303 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.82         |\n",
      "|    explained_variance   | 0.0167        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0489        |\n",
      "|    n_updates            | 1660          |\n",
      "|    policy_gradient_loss | -0.000481     |\n",
      "|    value_loss           | 0.145         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 168          |\n",
      "|    time_elapsed         | 6998         |\n",
      "|    total_timesteps      | 86016        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0002763929 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.82        |\n",
      "|    explained_variance   | 0.00201      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0385       |\n",
      "|    n_updates            | 1670         |\n",
      "|    policy_gradient_loss | -0.000878    |\n",
      "|    value_loss           | 0.143        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 169           |\n",
      "|    time_elapsed         | 7040          |\n",
      "|    total_timesteps      | 86528         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00018240884 |\n",
      "|    clip_fraction        | 0             |\n",
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      "|    entropy_loss         | -1.83         |\n",
      "|    explained_variance   | -0.0455       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0705        |\n",
      "|    n_updates            | 1680          |\n",
      "|    policy_gradient_loss | -0.000672     |\n",
      "|    value_loss           | 0.14          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 170           |\n",
      "|    time_elapsed         | 7082          |\n",
      "|    total_timesteps      | 87040         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00097097503 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.82         |\n",
      "|    explained_variance   | 0.246         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 162           |\n",
      "|    n_updates            | 1690          |\n",
      "|    policy_gradient_loss | -0.000377     |\n",
      "|    value_loss           | 513           |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 171          |\n",
      "|    time_elapsed         | 7124         |\n",
      "|    total_timesteps      | 87552        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 1.467939e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.81        |\n",
      "|    explained_variance   | -0.0259      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0578       |\n",
      "|    n_updates            | 1700         |\n",
      "|    policy_gradient_loss | -6.09e-06    |\n",
      "|    value_loss           | 0.684        |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 172          |\n",
      "|    time_elapsed         | 7165         |\n",
      "|    total_timesteps      | 88064        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0001691794 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.82        |\n",
      "|    explained_variance   | 0.0193       |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0599       |\n",
      "|    n_updates            | 1710         |\n",
      "|    policy_gradient_loss | -0.000949    |\n",
      "|    value_loss           | 0.294        |\n",
      "------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 173          |\n",
      "|    time_elapsed         | 7207         |\n",
      "|    total_timesteps      | 88576        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0001343633 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.82        |\n",
      "|    explained_variance   | -0.0236      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0684       |\n",
      "|    n_updates            | 1720         |\n",
      "|    policy_gradient_loss | -0.000197    |\n",
      "|    value_loss           | 0.218        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 174           |\n",
      "|    time_elapsed         | 7249          |\n",
      "|    total_timesteps      | 89088         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00024553062 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.8          |\n",
      "|    explained_variance   | 0.148         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 44.8          |\n",
      "|    n_updates            | 1730          |\n",
      "|    policy_gradient_loss | 0.000637      |\n",
      "|    value_loss           | 110           |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 175           |\n",
      "|    time_elapsed         | 7291          |\n",
      "|    total_timesteps      | 89600         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 3.8649072e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.8          |\n",
      "|    explained_variance   | 0.0206        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0931        |\n",
      "|    n_updates            | 1740          |\n",
      "|    policy_gradient_loss | 1.62e-05      |\n",
      "|    value_loss           | 0.696         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 176          |\n",
      "|    time_elapsed         | 7333         |\n",
      "|    total_timesteps      | 90112        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 2.646586e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.8         |\n",
      "|    explained_variance   | -0.07        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.056        |\n",
      "|    n_updates            | 1750         |\n",
      "|    policy_gradient_loss | 0.000156     |\n",
      "|    value_loss           | 0.601        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 177           |\n",
      "|    time_elapsed         | 7375          |\n",
      "|    total_timesteps      | 90624         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00013761909 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.8          |\n",
      "|    explained_variance   | -0.0391       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0511        |\n",
      "|    n_updates            | 1760          |\n",
      "|    policy_gradient_loss | -0.000553     |\n",
      "|    value_loss           | 0.388         |\n",
      "-------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 178          |\n",
      "|    time_elapsed         | 7417         |\n",
      "|    total_timesteps      | 91136        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 6.215391e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.8         |\n",
      "|    explained_variance   | 0.0214       |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0556       |\n",
      "|    n_updates            | 1770         |\n",
      "|    policy_gradient_loss | -0.000154    |\n",
      "|    value_loss           | 0.223        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 179           |\n",
      "|    time_elapsed         | 7459          |\n",
      "|    total_timesteps      | 91648         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 4.1826162e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.8          |\n",
      "|    explained_variance   | -0.0167       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0849        |\n",
      "|    n_updates            | 1780          |\n",
      "|    policy_gradient_loss | -0.000139     |\n",
      "|    value_loss           | 0.239         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 180          |\n",
      "|    time_elapsed         | 7500         |\n",
      "|    total_timesteps      | 92160        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 8.934201e-05 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.81        |\n",
      "|    explained_variance   | -0.0457      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0418       |\n",
      "|    n_updates            | 1790         |\n",
      "|    policy_gradient_loss | -0.000374    |\n",
      "|    value_loss           | 0.163        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 181           |\n",
      "|    time_elapsed         | 7542          |\n",
      "|    total_timesteps      | 92672         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00015510048 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.81         |\n",
      "|    explained_variance   | -0.00581      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0335        |\n",
      "|    n_updates            | 1800          |\n",
      "|    policy_gradient_loss | -0.000571     |\n",
      "|    value_loss           | 0.137         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 182           |\n",
      "|    time_elapsed         | 7584          |\n",
      "|    total_timesteps      | 93184         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00021820643 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.81         |\n",
      "|    explained_variance   | -0.0335       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.06          |\n",
      "|    n_updates            | 1810          |\n",
      "|    policy_gradient_loss | -0.00083      |\n",
      "|    value_loss           | 0.137         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 183           |\n",
      "|    time_elapsed         | 7625          |\n",
      "|    total_timesteps      | 93696         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00023878354 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.81         |\n",
      "|    explained_variance   | 0.0242        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0377        |\n",
      "|    n_updates            | 1820          |\n",
      "|    policy_gradient_loss | -0.000796     |\n",
      "|    value_loss           | 0.108         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 184          |\n",
      "|    time_elapsed         | 7667         |\n",
      "|    total_timesteps      | 94208        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0001907614 |\n",
      "|    clip_fraction        | 0            |\n",
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      "|    entropy_loss         | -1.81        |\n",
      "|    explained_variance   | -0.0258      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.0414       |\n",
      "|    n_updates            | 1830         |\n",
      "|    policy_gradient_loss | -0.000709    |\n",
      "|    value_loss           | 0.111        |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 185           |\n",
      "|    time_elapsed         | 7709          |\n",
      "|    total_timesteps      | 94720         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00012336986 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.8          |\n",
      "|    explained_variance   | -0.00253      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0471        |\n",
      "|    n_updates            | 1840          |\n",
      "|    policy_gradient_loss | -0.000564     |\n",
      "|    value_loss           | 0.104         |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 186          |\n",
      "|    time_elapsed         | 7751         |\n",
      "|    total_timesteps      | 95232        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0018382153 |\n",
      "|    clip_fraction        | 0.00293      |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.78        |\n",
      "|    explained_variance   | 0.301        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 138          |\n",
      "|    n_updates            | 1850         |\n",
      "|    policy_gradient_loss | -0.000126    |\n",
      "|    value_loss           | 396          |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 187           |\n",
      "|    time_elapsed         | 7792          |\n",
      "|    total_timesteps      | 95744         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00015607162 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.77         |\n",
      "|    explained_variance   | -0.025        |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.126         |\n",
      "|    n_updates            | 1860          |\n",
      "|    policy_gradient_loss | -0.000225     |\n",
      "|    value_loss           | 1.9           |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 188          |\n",
      "|    time_elapsed         | 7834         |\n",
      "|    total_timesteps      | 96256        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0028756773 |\n",
      "|    clip_fraction        | 0.00586      |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.75        |\n",
      "|    explained_variance   | 0.153        |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 139          |\n",
      "|    n_updates            | 1870         |\n",
      "|    policy_gradient_loss | -0.00146     |\n",
      "|    value_loss           | 321          |\n",
      "------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 189           |\n",
      "|    time_elapsed         | 7876          |\n",
      "|    total_timesteps      | 96768         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00014718226 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.73         |\n",
      "|    explained_variance   | 0.692         |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 6.49          |\n",
      "|    n_updates            | 1880          |\n",
      "|    policy_gradient_loss | -0.000221     |\n",
      "|    value_loss           | 21.5          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 190           |\n",
      "|    time_elapsed         | 7917          |\n",
      "|    total_timesteps      | 97280         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 1.9481522e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.73         |\n",
      "|    explained_variance   | -0.0226       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.277         |\n",
      "|    n_updates            | 1890          |\n",
      "|    policy_gradient_loss | 0.000678      |\n",
      "|    value_loss           | 1.77          |\n",
      "-------------------------------------------\n",
      "------------------------------------------\n",
      "| time/                   |              |\n",
      "|    fps                  | 12           |\n",
      "|    iterations           | 191          |\n",
      "|    time_elapsed         | 7960         |\n",
      "|    total_timesteps      | 97792        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0002280568 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.2          |\n",
      "|    entropy_loss         | -1.74        |\n",
      "|    explained_variance   | -0.0435      |\n",
      "|    learning_rate        | 1e-06        |\n",
      "|    loss                 | 0.189        |\n",
      "|    n_updates            | 1900         |\n",
      "|    policy_gradient_loss | -0.000742    |\n",
      "|    value_loss           | 1.09         |\n",
      "------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 192           |\n",
      "|    time_elapsed         | 8001          |\n",
      "|    total_timesteps      | 98304         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00011534197 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.74         |\n",
      "|    explained_variance   | -0.0243       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.13          |\n",
      "|    n_updates            | 1910          |\n",
      "|    policy_gradient_loss | -0.000201     |\n",
      "|    value_loss           | 1.07          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 193           |\n",
      "|    time_elapsed         | 8043          |\n",
      "|    total_timesteps      | 98816         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00021190918 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.75         |\n",
      "|    explained_variance   | -0.0199       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.138         |\n",
      "|    n_updates            | 1920          |\n",
      "|    policy_gradient_loss | -0.000838     |\n",
      "|    value_loss           | 0.611         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 194           |\n",
      "|    time_elapsed         | 8085          |\n",
      "|    total_timesteps      | 99328         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 4.2675994e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.76         |\n",
      "|    explained_variance   | -0.00344      |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.164         |\n",
      "|    n_updates            | 1930          |\n",
      "|    policy_gradient_loss | 0.000275      |\n",
      "|    value_loss           | 0.51          |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 195           |\n",
      "|    time_elapsed         | 8127          |\n",
      "|    total_timesteps      | 99840         |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 0.00015855033 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.75         |\n",
      "|    explained_variance   | -0.0142       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0709        |\n",
      "|    n_updates            | 1940          |\n",
      "|    policy_gradient_loss | -0.000825     |\n",
      "|    value_loss           | 0.401         |\n",
      "-------------------------------------------\n",
      "-------------------------------------------\n",
      "| time/                   |               |\n",
      "|    fps                  | 12            |\n",
      "|    iterations           | 196           |\n",
      "|    time_elapsed         | 8169          |\n",
      "|    total_timesteps      | 100352        |\n",
      "| train/                  |               |\n",
      "|    approx_kl            | 6.7998655e-05 |\n",
      "|    clip_fraction        | 0             |\n",
      "|    clip_range           | 0.2           |\n",
      "|    entropy_loss         | -1.75         |\n",
      "|    explained_variance   | -0.0277       |\n",
      "|    learning_rate        | 1e-06         |\n",
      "|    loss                 | 0.0675        |\n",
      "|    n_updates            | 1950          |\n",
      "|    policy_gradient_loss | -0.000441     |\n",
      "|    value_loss           | 0.263         |\n",
      "-------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<stable_baselines3.ppo.ppo.PPO at 0x7f649bc53370>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.learn(total_timesteps=100000, callback=callback)\n",
    "# Imagining that every single move is a frame, total-timesteps is the number of moves that our AI will make."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f413a78",
   "metadata": {},
   "source": [
    "# Test it out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2b957223",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load model\n",
    "model = PPO.load('./train/best_model_100000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5116b12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "state = env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "028289e7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/joy/.local/lib/python3.10/site-packages/stable_baselines3/common/vec_env/base_vec_env.py:234: UserWarning: You tried to call render() but no `render_mode` was passed to the env constructor.\n",
      "  warnings.warn(\"You tried to call render() but no `render_mode` was passed to the env constructor.\")\n",
      "/home/joy/.local/lib/python3.10/site-packages/gym_super_mario_bros/smb_env.py:148: RuntimeWarning: overflow encountered in ubyte_scalars\n",
      "  return (self.ram[0x86] - self.ram[0x071c]) % 256\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [21], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m      5\u001b[0m     action, _ \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(state)\n\u001b[0;32m----> 6\u001b[0m     state, reward, done, info \u001b[38;5;241m=\u001b[39m env\u001b[38;5;241m.\u001b[39mstep(action)\n\u001b[1;32m      7\u001b[0m     env\u001b[38;5;241m.\u001b[39mrender()\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/stable_baselines3/common/vec_env/base_vec_env.py:197\u001b[0m, in \u001b[0;36mVecEnv.step\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    190\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    191\u001b[0m \u001b[38;5;124;03mStep the environments with the given action\u001b[39;00m\n\u001b[1;32m    192\u001b[0m \n\u001b[1;32m    193\u001b[0m \u001b[38;5;124;03m:param actions: the action\u001b[39;00m\n\u001b[1;32m    194\u001b[0m \u001b[38;5;124;03m:return: observation, reward, done, information\u001b[39;00m\n\u001b[1;32m    195\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    196\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstep_async(actions)\n\u001b[0;32m--> 197\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep_wait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/stable_baselines3/common/vec_env/vec_frame_stack.py:33\u001b[0m, in \u001b[0;36mVecFrameStack.step_wait\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep_wait\u001b[39m(\n\u001b[1;32m     31\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m     32\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[Union[np\u001b[38;5;241m.\u001b[39mndarray, Dict[\u001b[38;5;28mstr\u001b[39m, np\u001b[38;5;241m.\u001b[39mndarray]], np\u001b[38;5;241m.\u001b[39mndarray, np\u001b[38;5;241m.\u001b[39mndarray, List[Dict[\u001b[38;5;28mstr\u001b[39m, Any]],]:\n\u001b[0;32m---> 33\u001b[0m     observations, rewards, dones, infos \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvenv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep_wait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     34\u001b[0m     observations, infos \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstacked_obs\u001b[38;5;241m.\u001b[39mupdate(observations, dones, infos)  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[1;32m     35\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m observations, rewards, dones, infos\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/stable_baselines3/common/vec_env/dummy_vec_env.py:58\u001b[0m, in \u001b[0;36mDummyVecEnv.step_wait\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep_wait\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m VecEnvStepReturn:\n\u001b[1;32m     56\u001b[0m     \u001b[38;5;66;03m# Avoid circular imports\u001b[39;00m\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m env_idx \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_envs):\n\u001b[0;32m---> 58\u001b[0m         obs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuf_rews[env_idx], terminated, truncated, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuf_infos[env_idx] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menvs\u001b[49m\u001b[43m[\u001b[49m\u001b[43menv_idx\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mactions\u001b[49m\u001b[43m[\u001b[49m\u001b[43menv_idx\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     60\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     61\u001b[0m         \u001b[38;5;66;03m# convert to SB3 VecEnv api\u001b[39;00m\n\u001b[1;32m     62\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuf_dones[env_idx] \u001b[38;5;241m=\u001b[39m terminated \u001b[38;5;129;01mor\u001b[39;00m truncated\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/shimmy/openai_gym_compatibility.py:257\u001b[0m, in \u001b[0;36mGymV21CompatibilityV0.step\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m    248\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, action: ActType) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[Any, \u001b[38;5;28mfloat\u001b[39m, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28mbool\u001b[39m, \u001b[38;5;28mdict\u001b[39m]:\n\u001b[1;32m    249\u001b[0m     \u001b[38;5;124;03m\"\"\"Steps through the environment.\u001b[39;00m\n\u001b[1;32m    250\u001b[0m \n\u001b[1;32m    251\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[38;5;124;03m        (observation, reward, terminated, truncated, info)\u001b[39;00m\n\u001b[1;32m    256\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 257\u001b[0m     obs, reward, done, info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgym_env\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    259\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrender_mode \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrender()\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/core.py:495\u001b[0m, in \u001b[0;36mObservationWrapper.step\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m    493\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, action):\n\u001b[1;32m    494\u001b[0m     \u001b[38;5;124;03m\"\"\"Returns a modified observation using :meth:`self.observation` after calling :meth:`env.step`.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 495\u001b[0m     step_returns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    496\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(step_returns) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m5\u001b[39m:\n\u001b[1;32m    497\u001b[0m         observation, reward, terminated, truncated, info \u001b[38;5;241m=\u001b[39m step_returns\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/nes_py/wrappers/joypad_space.py:74\u001b[0m, in \u001b[0;36mJoypadSpace.step\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;124;03mTake a step using the given action.\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     71\u001b[0m \n\u001b[1;32m     72\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     73\u001b[0m \u001b[38;5;66;03m# take the step and record the output\u001b[39;00m\n\u001b[0;32m---> 74\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_action_map\u001b[49m\u001b[43m[\u001b[49m\u001b[43maction\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/wrappers/time_limit.py:60\u001b[0m, in \u001b[0;36mTimeLimit.step\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, action):\n\u001b[1;32m     49\u001b[0m     \u001b[38;5;124;03m\"\"\"Steps through the environment and if the number of steps elapsed exceeds ``max_episode_steps`` then truncate.\u001b[39;00m\n\u001b[1;32m     50\u001b[0m \n\u001b[1;32m     51\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[38;5;124;03m        \"TimeLimit.truncated\"=False if the environment terminated\u001b[39;00m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m     59\u001b[0m     observation, reward, terminated, truncated, info \u001b[38;5;241m=\u001b[39m step_api_compatibility(\n\u001b[0;32m---> 60\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m     61\u001b[0m         \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m     62\u001b[0m     )\n\u001b[1;32m     63\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_elapsed_steps \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_elapsed_steps \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_max_episode_steps:\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/wrappers/order_enforcing.py:37\u001b[0m, in \u001b[0;36mOrderEnforcing.step\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_reset:\n\u001b[1;32m     36\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ResetNeeded(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot call env.step() before calling env.reset()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 37\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/wrappers/step_api_compatibility.py:52\u001b[0m, in \u001b[0;36mStepAPICompatibility.step\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstep\u001b[39m(\u001b[38;5;28mself\u001b[39m, action):\n\u001b[1;32m     44\u001b[0m     \u001b[38;5;124;03m\"\"\"Steps through the environment, returning 5 or 4 items depending on `new_step_api`.\u001b[39;00m\n\u001b[1;32m     45\u001b[0m \n\u001b[1;32m     46\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;124;03m        (observation, reward, terminated, truncated, info) or (observation, reward, done, info)\u001b[39;00m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 52\u001b[0m     step_returns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnew_step_api:\n\u001b[1;32m     54\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m step_to_new_api(step_returns)\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/gym/wrappers/env_checker.py:39\u001b[0m, in \u001b[0;36mPassiveEnvChecker.step\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m env_step_passive_checker(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39menv, action)\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 39\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/nes_py/nes_env.py:293\u001b[0m, in \u001b[0;36mNESEnv.step\u001b[0;34m(self, action)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrollers[\u001b[38;5;241m0\u001b[39m][:] \u001b[38;5;241m=\u001b[39m action\n\u001b[1;32m    292\u001b[0m \u001b[38;5;66;03m# pass the action to the emulator as an unsigned byte\u001b[39;00m\n\u001b[0;32m--> 293\u001b[0m \u001b[43m_LIB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mStep\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_env\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[38;5;66;03m# get the reward for this step\u001b[39;00m\n\u001b[1;32m    295\u001b[0m reward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_reward()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Start the game\n",
    "state = env.reset()\n",
    "# Loop through the game\n",
    "while True:\n",
    "    action, _ = model.predict(state)\n",
    "    state, reward, done, info = env.step(action)\n",
    "    env.render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a68b8704",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gym-super-mario-bros==7.3.0\n",
    "# nes-py==8.1.8\n",
    "# gym==0.25.1\n",
    "# stable-baselines3==2.1.0\n",
    "# opencv-python==4.8.0.76"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90e74fb8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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