driving-tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9507245a-a8eb-4fe3-ba27-fd5d889361ac",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import gymnasium as gym\n",
    "from stable_baselines3 import PPO\n",
    "from stable_baselines3.common.vec_env import DummyVecEnv\n",
    "from stable_baselines3.common.evaluation import evaluate_policy\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "13062fc1-96aa-4c7b-9ecd-24a8a1344944",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "environment_name = 'CarRacing-v2'\n",
    "env = gym.make(environment_name, render_mode='human')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdd70038-971c-41cd-8d04-4d7de7a058c6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07ffa017-6af7-407b-a10d-4d843719cefc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "env = gym.make(environment_name, render_mode='human')\n",
    "episodes = 5\n",
    "for episode in range(1, episodes+1):\n",
    "    obs = env.reset()\n",
    "    done = False\n",
    "    score = 0\n",
    "    \n",
    "    while not done:\n",
    "        env.render()\n",
    "        action = env.action_space.sample()\n",
    "        obs, reward, done, info, frame = env.step(action)\n",
    "        score+=reward\n",
    "    print('Episode:{} Score:{}'.format(episode,score))\n",
    "\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44e46b2e-6d29-4365-b7f8-b4ddc7a8f08c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "env = gym.make(environment_name)\n",
    "env = DummyVecEnv([lambda: env])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9551ce5f-65a1-4a22-ae3d-a0bb120adaa8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cpu device\n",
      "Wrapping the env in a VecTransposeImage.\n"
     ]
    }
   ],
   "source": [
    "log_path = os.path.join('Training', 'Logs')\n",
    "model = PPO('CnnPolicy', env, verbose=1, tensorboard_log=log_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a7c8af1-f21f-4142-9c96-2874967012ea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\npath_id = 1\\nfor i in range(10):\\n    model.learn(300000)\\n    ppo_path = os.path.join('Training', 'Saved Models', f'Driving_Model_{path_id}')\\n    model.save(ppo_path)\\n    path_id += 1\\n    \\n\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "path_id = 1\n",
    "for i in range(10):\n",
    "    model.learn(300000)\n",
    "    ppo_path = os.path.join('Training', 'Saved Models', f'Driving_Model_{path_id}')\n",
    "    model.save(ppo_path)\n",
    "    path_id += 1\n",
    "    \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "189edaa8-209a-4df8-97f9-bee8e6065261",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ppo_path = os.path.join('Training', 'Saved Models', 'Driving_Model_5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "773e6251-05fb-4e79-a434-24188dfdf07c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model.save(ppo_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50eab803-84bb-4a7a-82c0-18953eb6da62",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "del model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a3c56b3e-6d93-4ec7-802d-3388c92b8ef1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Wrapping the env in a VecTransposeImage.\n"
     ]
    }
   ],
   "source": [
    "env = gym.make(environment_name, render_mode='human')\n",
    "model = PPO.load(ppo_path, env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "06073273-1d23-4da3-b4a4-13e8d49f41d0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(730.839423839748, 188.16948285093258)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate_policy(model, env, n_eval_episodes=10, render=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "733c0232-7582-4a4a-bc00-dff7b1c06c34",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fefa831f-44a4-409e-8524-51241252e4b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Wrapping the env in a VecTransposeImage.\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    ppo_path = os.path.join('Training', 'Saved Models', f'Driving_Model_{i+1}')\n",
    "    env = gym.make(environment_name, render_mode='human')\n",
    "    model = PPO.load(ppo_path, env)\n",
    "    evaluate_policy(model, env, n_eval_episodes=10, render=True)\n",
    "    env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf38257a-a244-4608-823b-8160d3605760",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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