{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3ae1079",
   "metadata": {},
   "source": [
    "# Training\n",
    "\n",
    "[![Click and Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/metadriverse/metadrive/blob/main/documentation/source/training.ipynb)\n",
    "\n",
    "In this section, we provide examples about how to use common RL frameworks to train autonomous driving policy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4001744",
   "metadata": {},
   "source": [
    "## stable-baselines3\n",
    "[stable-baselines3](https://github.com/DLR-RM/stable-baselines3) is a lightweight RL training framework, providing most of the commonly used algorithms. It is easy to use on desktop PC, personal laptops, and single node servers. To run the following mini example, let's install the *stable-baselines3* and dependencies at first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2a12ab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install stable-baselines3[extra]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008cb056",
   "metadata": {},
   "source": [
    "\n",
    "Training a driving agent with *stable-baselines3* is easy and straightforward. In the following example, we train an agent to drive in about 5 minutes. \n",
    "Let's create a simple environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b4ef6731",
   "metadata": {},
   "outputs": [],
   "source": [
    "from metadrive.envs import MetaDriveEnv\n",
    "from metadrive.policy.lange_change_policy import LaneChangePolicy\n",
    "import matplotlib.pyplot as plt\n",
    "from stable_baselines3.common.monitor import Monitor\n",
    "from metadrive.component.map.base_map import BaseMap\n",
    "from metadrive.utils.doc_utils import generate_gif\n",
    "from IPython.display import Image\n",
    "\n",
    "def create_env(need_monitor=False):\n",
    "    env = MetaDriveEnv(dict(map=\"C\",\n",
    "                      # This policy setting simplifies the task                      \n",
    "                      discrete_action=True,\n",
    "                      discrete_throttle_dim=3,\n",
    "                      discrete_steering_dim=3,\n",
    "                      horizon=500,\n",
    "                      # scenario setting\n",
    "                      random_spawn_lane_index=False,\n",
    "                      num_scenarios=1,\n",
    "                      start_seed=5,\n",
    "                      traffic_density=0,\n",
    "                      accident_prob=0,\n",
    "                      log_level=50))\n",
    "    if need_monitor:\n",
    "        env = Monitor(env)\n",
    "    return env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "edbe6182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f923e770bd0>"
      ]
     },
     "execution_count": 3,
     "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": [
    "env=create_env()\n",
    "env.reset()\n",
    "ret = env.render(mode=\"topdown\", \n",
    "                 window=False,\n",
    "                 screen_size=(600, 600), \n",
    "                 camera_position=(50, 50))\n",
    "env.close()\n",
    "plt.axis(\"off\")\n",
    "plt.imshow(ret)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9275db",
   "metadata": {},
   "source": [
    "After defining the env creation function, we can use the `SubProcVecEnv` to launch several MetaDrive instances and train the driving agent with PPO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "400cc353",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training is finished! Generate gif ...\n",
      "episode_reward 168.7058092369063\n",
      "gif generation is finished ...\n"
     ]
    }
   ],
   "source": [
    "import gymnasium as gym\n",
    "from stable_baselines3 import PPO\n",
    "from stable_baselines3.common.vec_env.subproc_vec_env import SubprocVecEnv\n",
    "from stable_baselines3.common.utils import set_random_seed\n",
    "from functools import partial\n",
    "from IPython.display import clear_output\n",
    "import os\n",
    "\n",
    "set_random_seed(0)\n",
    "# 4 subprocess to rollout\n",
    "train_env=SubprocVecEnv([partial(create_env, True) for _ in range(4)]) \n",
    "model = PPO(\"MlpPolicy\", \n",
    "            train_env,\n",
    "            n_steps=4096,\n",
    "            verbose=1)\n",
    "model.learn(total_timesteps=1000 if os.getenv('TEST_DOC') else 300_000,\n",
    "            log_interval=4)\n",
    "\n",
    "clear_output()\n",
    "print(\"Training is finished! Generate gif ...\")\n",
    "\n",
    "# evaluation\n",
    "total_reward = 0\n",
    "env=create_env()\n",
    "obs, _ = env.reset()\n",
    "try:\n",
    "    for i in range(1000):\n",
    "        action, _states = model.predict(obs, deterministic=True)\n",
    "        obs, reward, done, _, info = env.step(action)\n",
    "        total_reward += reward\n",
    "        ret = env.render(mode=\"topdown\", \n",
    "                         screen_record=True,\n",
    "                         window=False,\n",
    "                         screen_size=(600, 600), \n",
    "                         camera_position=(50, 50))\n",
    "        if done:\n",
    "            print(\"episode_reward\", total_reward)\n",
    "            break\n",
    "            \n",
    "    env.top_down_renderer.generate_gif()\n",
    "finally:\n",
    "    env.close()\n",
    "print(\"gif generation is finished ...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ac9a341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(open(\"demo.gif\", 'rb').read())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9798ff95",
   "metadata": {},
   "source": [
    "## ray/rllib\n",
    "\n",
    "We provide a script demonstrating how to use [RLLib>=2.2.0](https://docs.ray.io/en/latest/rllib.html) to train generalizable RL agents:\n",
    "\n",
    "```bash\n",
    "# Make sure current folder does not have a sub-folder named metadrive\n",
    "python -m metadrive.examples.train_generalization_experiment\n",
    "\n",
    "# You can also use GPUs and customized experiment name:\n",
    "python -m metadrive.examples.train_generalization_experiment \\\n",
    " --exp-name CUSTOMIZED_EXP_NAME \\\n",
    " --num-gpus HOW_MANY_GPUS_IN_THIS_MACHINES\n",
    "```\n",
    "\n",
    "In this example, we leave the training hyper-parameter `config[\"num_envs_per_worker\"] = 1` as default, so that each process (ray worker) will only contain one MetaDrive instance.\n",
    "We further set the evaluation workers `config[\"evaluation_num_workers\"] = 5`, so that the test set environments are hosted in separated processes.\n",
    "By utilizing the feature of RLLib, we avoid the issue of multiple MetaDrive instances in single process. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "582b14da",
   "metadata": {},
   "source": [
    "We welcome more examples using MetaDrive in different context! Please show off your code if you like to share it by opening new issue! Thanks!"
   ]
  }
 ],
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    "name": "ipython",
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