Mario-Train-Evaluate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "37d63118",
   "metadata": {},
   "source": [
    "# 1. Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "21f9b277",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\MarioPPO\\Super-mario reinforcement learning\\ppo\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from nes_py.wrappers import JoypadSpace\n",
    "import gym \n",
    "import gym_super_mario_bros\n",
    "from gym_super_mario_bros.actions import SIMPLE_MOVEMENT, RIGHT_ONLY \n",
    "\n",
    "from stable_baselines3 import PPO\n",
    "from stable_baselines3.common.vec_env import DummyVecEnv, VecEnv, SubprocVecEnv\n",
    "from stable_baselines3.common.evaluation import evaluate_policy\n",
    "from stable_baselines3.common.monitor import Monitor\n",
    "from stable_baselines3.common.callbacks import BaseCallback\n",
    "\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os\n",
    "\n",
    "from gym_utils import SMBRamWrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5ccf1d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "torch. cuda. is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cf3dac61",
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym_super_mario_bros.make('SuperMarioBros-2-1-v0')\n",
    "env = JoypadSpace(env, SIMPLE_MOVEMENT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec773972",
   "metadata": {},
   "source": [
    "# 2. Process Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecdffe89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup cropping size\n",
    "x0 = 0\n",
    "x1 = 16\n",
    "y0 = 0\n",
    "y1 = 13\n",
    "n_stack = 4\n",
    "n_skip = 4\n",
    "\n",
    "env_wrap = SMBRamWrapper(env, [x0, x1, y0, y1], n_stack=n_stack, n_skip=n_skip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f1018d94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test env_wrap\n",
    "done = True\n",
    "for i in range(150):\n",
    "    if done:\n",
    "        state = env_wrap.reset()\n",
    "    state, reward, done, info = env_wrap.step(env_wrap.action_space.sample())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be737c90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13, 16, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4cad5f86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WHAT WE SEE\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def see_world(test_env):\n",
    "  obs = test_env.reset()\n",
    "  for i in range(10):\n",
    "      action = test_env.action_space.sample()\n",
    "      obs, reward, done, info = test_env.step(action)\n",
    "\n",
    "  from matplotlib import pyplot as plt\n",
    "  if obs.shape[2] == 4:\n",
    "    plt.imshow(obs[-1])\n",
    "    plt.axis('off')\n",
    "  else:\n",
    "    plt.imshow(obs)\n",
    "    plt.axis('off')\n",
    "  plt.show()\n",
    "print(\"WHAT WE SEE\")\n",
    "see_world(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "503a7d85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WHAT MODEL SEE\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"WHAT MODEL SEE\")\n",
    "fig, ax = plt.subplots(1, n_stack, figsize=(14,10))\n",
    "for i in range(n_stack):\n",
    "    ax[i].imshow(state[:,:,n_stack-i-1], vmin=-1, vmax=2)\n",
    "    ax[i].axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc903d16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply other wrapper functions\n",
    "env_wrap = Monitor(env_wrap)  # for tensorboard log\n",
    "env_wrap = DummyVecEnv([lambda: env_wrap])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1f4a86f",
   "metadata": {},
   "source": [
    "# 3. Setup RL Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "09f25b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Callbacks\n",
    "\n",
    "# Save intermediate models\n",
    "class TrainAndLoggingCallback(BaseCallback):\n",
    "\n",
    "    def __init__(self, check_freq, save_path, \n",
    "                 starting_steps=0, verbose=1):\n",
    "        super(TrainAndLoggingCallback, self).__init__(verbose)\n",
    "        self.check_freq = check_freq\n",
    "        self.save_path = save_path\n",
    "        self.starting_steps = starting_steps\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 + int(self.starting_steps)))\n",
    "            self.model.save(model_path)\n",
    "\n",
    "        return True\n",
    "    \n",
    "# Linear learning rate schedule\n",
    "# https://stable-baselines3.readthedocs.io/en/master/guide/examples.html#learning-rate-schedule\n",
    "from typing import Callable\n",
    "\n",
    "def linear_schedule(initial_value: float) -> Callable[[float], float]:\n",
    "    \"\"\"\n",
    "    Linear learning rate schedule.\n",
    "\n",
    "    :param initial_value: Initial learning rate.\n",
    "    :return: schedule that computes\n",
    "      current learning rate depending on remaining progress\n",
    "    \"\"\"\n",
    "    def func(progress_remaining: float) -> float:\n",
    "        \"\"\"\n",
    "        Progress will decrease from 1 (beginning) to 0.\n",
    "\n",
    "        :param progress_remaining:\n",
    "        :return: current learning rate\n",
    "        \"\"\"\n",
    "        return progress_remaining * initial_value\n",
    "\n",
    "    return func"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d20a1e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "### MODIFY THESE TWO DIRECTORIES BEFORE TRAINING A NEW MODEL ###\n",
    "MODEL_DIR = './models/NEW_MODEL_DIR2-1'\n",
    "LOG_DIR = './logs/NEW_LOG_DIR'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3b9d9b34",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = PPO('MlpPolicy', env_wrap, verbose=0, learning_rate=linear_schedule(3e-4), tensorboard_log=LOG_DIR) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f2b67e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "callback = TrainAndLoggingCallback(check_freq=1e5, starting_steps=0, save_path=MODEL_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e212fb30",
   "metadata": {},
   "source": [
    "# Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a257e97d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = PPO.load('./models/Best Models/level_1-4', env=env_wrap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dd9b8e81",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[28], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m t_start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m----> 3\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlearn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtotal_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m20e6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m t_elapsed \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m t_start\n",
      "File \u001b[1;32mD:\\MarioPPO\\Super-mario reinforcement learning\\ppo\\lib\\site-packages\\stable_baselines3\\ppo\\ppo.py:304\u001b[0m, in \u001b[0;36mPPO.learn\u001b[1;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[0;32m    291\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlearn\u001b[39m(\n\u001b[0;32m    292\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    293\u001b[0m     total_timesteps: \u001b[38;5;28mint\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    301\u001b[0m     reset_num_timesteps: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m    302\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPPO\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 304\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mPPO\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlearn\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    305\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtotal_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtotal_timesteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    306\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    307\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlog_interval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_interval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    308\u001b[0m \u001b[43m        \u001b[49m\u001b[43meval_env\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    309\u001b[0m \u001b[43m        \u001b[49m\u001b[43meval_freq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_freq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    310\u001b[0m \u001b[43m        \u001b[49m\u001b[43mn_eval_episodes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_eval_episodes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    311\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtb_log_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtb_log_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    312\u001b[0m \u001b[43m        \u001b[49m\u001b[43meval_log_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_log_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    313\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreset_num_timesteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset_num_timesteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    314\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\MarioPPO\\Super-mario reinforcement learning\\ppo\\lib\\site-packages\\stable_baselines3\\common\\on_policy_algorithm.py:270\u001b[0m, in \u001b[0;36mOnPolicyAlgorithm.learn\u001b[1;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[0;32m    267\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mrecord(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime/total_timesteps\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_timesteps, exclude\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtensorboard\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    268\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdump(step\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_timesteps)\n\u001b[1;32m--> 270\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    272\u001b[0m callback\u001b[38;5;241m.\u001b[39mon_training_end()\n\u001b[0;32m    274\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n",
      "File \u001b[1;32mD:\\MarioPPO\\Super-mario reinforcement learning\\ppo\\lib\\site-packages\\stable_baselines3\\ppo\\ppo.py:203\u001b[0m, in \u001b[0;36mPPO.train\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    200\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_sde:\n\u001b[0;32m    201\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpolicy\u001b[38;5;241m.\u001b[39mreset_noise(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_size)\n\u001b[1;32m--> 203\u001b[0m values, log_prob, entropy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpolicy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_actions\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrollout_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobservations\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mactions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    204\u001b[0m values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[0;32m    205\u001b[0m \u001b[38;5;66;03m# Normalize advantage\u001b[39;00m\n",
      "File \u001b[1;32mD:\\MarioPPO\\Super-mario reinforcement learning\\ppo\\lib\\site-packages\\stable_baselines3\\common\\policies.py:646\u001b[0m, in \u001b[0;36mActorCriticPolicy.evaluate_actions\u001b[1;34m(self, obs, actions)\u001b[0m\n\u001b[0;32m    644\u001b[0m latent_pi, latent_vf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmlp_extractor(features)\n\u001b[0;32m    645\u001b[0m distribution \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_action_dist_from_latent(latent_pi)\n\u001b[1;32m--> 646\u001b[0m log_prob \u001b[38;5;241m=\u001b[39m \u001b[43mdistribution\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43mactions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    647\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalue_net(latent_vf)\n\u001b[0;32m    648\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m values, log_prob, distribution\u001b[38;5;241m.\u001b[39mentropy()\n",
      "File \u001b[1;32mD:\\MarioPPO\\Super-mario reinforcement learning\\ppo\\lib\\site-packages\\stable_baselines3\\common\\distributions.py:278\u001b[0m, in \u001b[0;36mCategoricalDistribution.log_prob\u001b[1;34m(self, actions)\u001b[0m\n\u001b[0;32m    277\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlog_prob\u001b[39m(\u001b[38;5;28mself\u001b[39m, actions: th\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m th\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[1;32m--> 278\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[43mdistribution\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43mactions\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mD:\\MarioPPO\\Super-mario reinforcement learning\\ppo\\lib\\site-packages\\torch\\distributions\\categorical.py:125\u001b[0m, in \u001b[0;36mCategorical.log_prob\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m    123\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_sample(value)\n\u001b[0;32m    124\u001b[0m value \u001b[38;5;241m=\u001b[39m value\u001b[38;5;241m.\u001b[39mlong()\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m--> 125\u001b[0m value, log_pmf \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast_tensors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogits\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    126\u001b[0m value \u001b[38;5;241m=\u001b[39m value[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, :\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m    127\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m log_pmf\u001b[38;5;241m.\u001b[39mgather(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, value)\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[1;32mD:\\MarioPPO\\Super-mario reinforcement learning\\ppo\\lib\\site-packages\\torch\\functional.py:74\u001b[0m, in \u001b[0;36mbroadcast_tensors\u001b[1;34m(*tensors)\u001b[0m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function(tensors):\n\u001b[0;32m     73\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(broadcast_tensors, tensors, \u001b[38;5;241m*\u001b[39mtensors)\n\u001b[1;32m---> 74\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast_tensors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "t_start = time.time()\n",
    "\n",
    "model.learn(total_timesteps=20e6, callback=callback)\n",
    "\n",
    "t_elapsed = time.time() - t_start"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be236c1b",
   "metadata": {},
   "source": [
    "# Save and load trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0e2ef893",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save model\n",
    "model_path = os.path.join(MODEL_DIR, 'level_2-1')\n",
    "model.save(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "862a325b",
   "metadata": {},
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3dd36cca",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Load model\n",
    "LOG_DIR = './logs/SAVED_LOG_DIR'\n",
    "MODEL_DIR = './models/Best Models'\n",
    "\n",
    "model_path = os.path.join(MODEL_DIR, 'level_2-1')\n",
    "model = PPO.load(model_path, env=env_wrap)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d15d11ab",
   "metadata": {},
   "source": [
    "# Open tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3b606aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "LOG_NAME = 'PPO_1'\n",
    "TB_LOG = os.path.join(LOG_DIR, LOG_NAME)\n",
    "\n",
    "!tensorboard --logdir={TB_LOG}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "830f39a6",
   "metadata": {},
   "source": [
    "# 4. Test the Trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c24740d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from stable_baselines3.common.evaluation import evaluate_policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d355aa72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ActorCriticPolicy(\n",
      "  (features_extractor): FlattenExtractor(\n",
      "    (flatten): Flatten(start_dim=1, end_dim=-1)\n",
      "  )\n",
      "  (mlp_extractor): MlpExtractor(\n",
      "    (shared_net): Sequential()\n",
      "    (policy_net): Sequential(\n",
      "      (0): Linear(in_features=832, out_features=64, bias=True)\n",
      "      (1): Tanh()\n",
      "      (2): Linear(in_features=64, out_features=64, bias=True)\n",
      "      (3): Tanh()\n",
      "    )\n",
      "    (value_net): Sequential(\n",
      "      (0): Linear(in_features=832, out_features=64, bias=True)\n",
      "      (1): Tanh()\n",
      "      (2): Linear(in_features=64, out_features=64, bias=True)\n",
      "      (3): Tanh()\n",
      "    )\n",
      "  )\n",
      "  (action_net): Linear(in_features=64, out_features=7, bias=True)\n",
      "  (value_net): Linear(in_features=64, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model.policy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a7b2be01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2129.0, 0.0)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_policy(model, env_wrap, n_eval_episodes=1, deterministic=True, render=False, return_episode_rewards=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1453a891",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\MarioPPO\\Super-mario reinforcement learning\\ppo\\lib\\site-packages\\pyglet\\image\\codecs\\wic.py:289: UserWarning: [WinError -2147417850] Cannot change thread mode after it is set\n",
      "  warnings.warn(str(err))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode:1 Score:[2129.]\n"
     ]
    }
   ],
   "source": [
    "episode = 1\n",
    "\n",
    "for episode in range(1, episode+1):\n",
    "    states = env_wrap.reset()\n",
    "    done = False\n",
    "    score = 0\n",
    "    \n",
    "    while not done:\n",
    "        env_wrap.render()\n",
    "        action, _ = model.predict(states, deterministic=True)\n",
    "        states, reward, done, info = env_wrap.step(action)\n",
    "        score += reward\n",
    "        time.sleep(0.01)\n",
    "    print('Episode:{} Score:{}'.format(episode, score))\n",
    "env.close()"
   ]
  }
 ],
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  "kernelspec": {
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   "codemirror_mode": {
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    "version": 3
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   "file_extension": ".py",
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