Train_DQN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\lynch\\anaconda3\\envs\\DQN_win\\lib\\site-packages\\tqdm\\auto.py:22: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================================================\n",
      "Device set to : NVIDIA GeForce RTX 3070 Laptop GPU\n",
      "============================================================================================\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "from gym import envs\n",
    "from gym import wrappers, logger\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torchvision\n",
    "import torchvision.transforms as T\n",
    "from PIL import Image\n",
    "import math\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import pandas as pd\n",
    "from utility import Config_DQN\n",
    "from agent.dqnagent import Agent\n",
    "from collections import deque\n",
    "from tqdm import tqdm\n",
    "from pathlib import Path\n",
    "from utility import get_unique_numbers\n",
    "import time\n",
    "################################## set device ##################################\n",
    "print(\"============================================================================================\")\n",
    "# set device to cpu or cuda\n",
    "if(torch.cuda.is_available()): \n",
    "    device = torch.device('cuda:0') \n",
    "    torch.cuda.empty_cache()\n",
    "    print(\"Device set to : \" + str(torch.cuda.get_device_name(device)))\n",
    "else:\n",
    "    device = torch.device('cpu')\n",
    "    print(\"Device set to : cpu\")\n",
    "print(\"============================================================================================\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create the storage folder and generate log file \n",
    "## Folder route\n",
    "* The folder is saved under the route describes in the cell, (mainly 'document/model/``file_name``')\n",
    "* The ``file_name`` is the name of storag folder for current training.\n",
    "\n",
    "\n",
    "\n",
    "## Set reward function configuration car racing environment\n",
    "1. In the car racing environment, the [car_racing.py](gym/envs/box2d/car_racing.py) contains the reward function configuration where user can play around for designing the risk awareness that the agent targeted to learn.\n",
    "2. Can modify the configuration of parameters before training, the log files are collected under the same directory.\n",
    "   * The configuration file defines the training episodes, frequency of saving learned model in `*.pth` files for both actor and critic networks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log File was created.\n"
     ]
    }
   ],
   "source": [
    "# create folder\n",
    "file_name = 'DQN' # Folder name\n",
    "p_model = (Path()/ 'document'/'model'/file_name) # storage under model directory\n",
    "p_model.mkdir(parents=True, exist_ok=True) # create if no directory existed\n",
    "\n",
    "\n",
    "# Track\n",
    "# load_tracks_from=TRACK -- Load a specific map from a pkl file\n",
    "TRACK = 'track'\n",
    "\n",
    "# ENV\n",
    "# Gym make the environment\n",
    "frame = int(1) # state is frame - 1\n",
    "env = gym.make('CarRacing-v0', num_obstacles=100, show_info_panel=1,\n",
    "               num_tracks=1, num_lanes=1, discretize_actions='soft', \n",
    "               allow_outside=False, grayscale=1, frames_per_state=frame,load_tracks_from = TRACK)\n",
    "\n",
    "observation_space = env.observation_space.shape\n",
    "action_space = env.action_space.n\n",
    "\n",
    "# Parameters\n",
    "\"\"\" configuration of parameter setup, and setup training configuration. \"\"\"\n",
    "# modify under\n",
    "Episode_train = 100           # Total training episodes\n",
    "Model_Save_frequency = 20    # the frequency of saving model under directory\n",
    "Time_stamp = 500            # Exploring time stamps\n",
    "seed = 32\n",
    "\n",
    "cfg = Config_DQN(print_ep=1, freq_save_epi=Model_Save_frequency, n_episodes=Episode_train, GAMMA=0.95, SEED=seed, eps_end=0.001, max_t=Time_stamp)\n",
    "setattr(cfg, 'state_size', observation_space)\n",
    "setattr(cfg, 'action_size', action_space) \n",
    "\n",
    "# Create an agent\n",
    "agent = Agent(cfg)\n",
    "\n",
    "\n",
    "if p_model.is_dir():\n",
    "    nb_name = 'log_par.txt'\n",
    "    with open(p_model.joinpath(nb_name), \"w\") as f:\n",
    "        f.write('-----------------------Var Config-----------------------\\n')\n",
    "        for key, value in vars(cfg).items(): \n",
    "            f.write('%s:%s\\n' % (key, value))\n",
    "        f.write('-----------------------Var Env-----------------------\\n')\n",
    "        for key, value in vars(env).items():\n",
    "            f.write('%s:%s\\n' % (key, value))\n",
    "        f.write('-----------------------Var File-----------------------\\n')\n",
    "        f.write('Directroy: %s,\\n'% p_model)\n",
    "        f.write('SOFT_NEG_REWARD = 0.1, \\nOut side: -1, \\nreward += ((((left | right) & not_visited).sum()+0.4)/ factor),\\n max_time_out = 10.0,\\ncheck_outside == False\\n')\n",
    "        print('Log File was created.')\n",
    "        f.close()\n",
    "else:\n",
    "    print(\"Directory doesn\\'t exist.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training function for DQN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DQN(cfg):\n",
    "    n_episodes = cfg.n_episodes\n",
    "    max_t = cfg.max_t\n",
    "    eps_start= cfg.eps_start\n",
    "    scores_deque = deque(maxlen=100)\n",
    "    scores = []\n",
    "    avg_scores = []\n",
    "    eps = eps_start\n",
    "\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "        state = env.reset()\n",
    "        score = 0\n",
    "        for t in range(max_t):\n",
    "            action = agent.act(state, eps)\n",
    "            next_state, reward, done, _ = env.step(action)\n",
    "            agent.step(state, action, reward, next_state, done)\n",
    "            state = next_state\n",
    "            \n",
    "            score += reward\n",
    "            if done:\n",
    "                break\n",
    "        scores_deque.append(score)\n",
    "        scores.append(score)\n",
    "        eps = max(cfg.eps_end, cfg.eps_decay*eps) # decrease epsilon        \n",
    "        avg_scores.append(np.mean(scores_deque))\n",
    "        if i_episode % cfg.print_ep ==0:\n",
    "            print('\\rEpisode {}\\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_deque)))   \n",
    "        if i_episode % cfg.freq_save_epi == 0:\n",
    "            local_name = f'DQN_local{i_episode}.pth'\n",
    "            torch.save(agent.qnetwork_local.state_dict(), (p_model/local_name))\n",
    "\n",
    "    return scores,avg_scores, agent"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start training\n",
    "\n",
    "The following cell start training for the agent.\n",
    "\n",
    "* Total training time are logged as `Run_log_training.txt` and will storage under the same directory.\n",
    "* The learned models are storaged as the frequency of storaging episodes as setted up above. (default is `every 2 episode save model once.`)\n",
    "* Plot for average scores and all scores are illustrated.\n",
    "  * The score plot shows the score of the end result of single episode.\n",
    "  * The average plot shows the averaging of last 100 time stamps' scoring. (in one episode)\n",
    "    > Since the agent can result in a lower score, for instance, by hitting on the obstacle after exploring for a while on the track. Collision does not represent the total actions what the agent already done.\n",
    "\n",
    "* The csv storages the score and average scores are under the same directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 1\tAverage Score: 82.00\n",
      "Episode 2\tAverage Score: 82.00\n",
      "Episode 3\tAverage Score: 82.33\n",
      "Episode 4\tAverage Score: 82.25\n",
      "Episode 5\tAverage Score: 82.40\n",
      "Episode 6\tAverage Score: 63.79\n",
      "Episode 7\tAverage Score: 66.68\n",
      "Episode 8\tAverage Score: 68.59\n",
      "Episode 9\tAverage Score: 70.75\n",
      "Episode 10\tAverage Score: 71.88\n",
      "Episode 11\tAverage Score: 72.61\n",
      "Episode 12\tAverage Score: 73.40\n",
      "Episode 13\tAverage Score: 62.57\n",
      "Episode 14\tAverage Score: 64.10\n",
      "Episode 15\tAverage Score: 19.98\n",
      "Episode 16\tAverage Score: 13.40\n",
      "Episode 17\tAverage Score: 9.42\n",
      "Episode 18\tAverage Score: 13.62\n",
      "Episode 19\tAverage Score: 17.32\n",
      "Episode 20\tAverage Score: -11.77\n",
      "Episode 21\tAverage Score: -13.81\n",
      "Episode 22\tAverage Score: -16.62\n",
      "Episode 23\tAverage Score: -12.46\n",
      "Episode 24\tAverage Score: -8.27\n",
      "Episode 25\tAverage Score: -4.30\n",
      "Episode 26\tAverage Score: -6.30\n",
      "Episode 27\tAverage Score: -7.23\n",
      "Episode 28\tAverage Score: -8.43\n",
      "Episode 29\tAverage Score: -9.00\n",
      "Episode 30\tAverage Score: -6.00\n",
      "Episode 31\tAverage Score: -23.37\n",
      "Episode 32\tAverage Score: -40.82\n",
      "Episode 33\tAverage Score: -42.30\n",
      "Episode 34\tAverage Score: -43.09\n",
      "Episode 35\tAverage Score: -43.89\n",
      "Episode 36\tAverage Score: -43.40\n",
      "Episode 37\tAverage Score: -43.56\n",
      "Episode 38\tAverage Score: -43.09\n",
      "Episode 39\tAverage Score: -43.79\n",
      "Episode 40\tAverage Score: -55.97\n",
      "Episode 41\tAverage Score: -55.76\n",
      "Episode 42\tAverage Score: -55.77\n",
      "Episode 43\tAverage Score: -52.47\n",
      "Episode 44\tAverage Score: -49.39\n",
      "Episode 45\tAverage Score: -46.54\n",
      "Episode 46\tAverage Score: -43.81\n",
      "Episode 47\tAverage Score: -41.20\n",
      "Episode 48\tAverage Score: -41.74\n",
      "Episode 49\tAverage Score: -42.64\n",
      "Episode 50\tAverage Score: -43.57\n",
      "Episode 51\tAverage Score: -44.09\n",
      "Episode 52\tAverage Score: -43.74\n",
      "Episode 53\tAverage Score: -43.40\n",
      "Episode 54\tAverage Score: -43.08\n",
      "Episode 55\tAverage Score: -42.76\n",
      "Episode 56\tAverage Score: -42.46\n",
      "Episode 57\tAverage Score: -40.33\n",
      "Episode 58\tAverage Score: -38.29\n",
      "Episode 59\tAverage Score: -47.77\n",
      "Episode 60\tAverage Score: -47.41\n",
      "Episode 61\tAverage Score: -47.02\n",
      "Episode 62\tAverage Score: -46.68\n",
      "Episode 63\tAverage Score: -46.35\n",
      "Episode 64\tAverage Score: -44.41\n",
      "Episode 65\tAverage Score: -44.28\n",
      "Episode 66\tAverage Score: -42.42\n",
      "Episode 67\tAverage Score: -42.50\n",
      "Episode 68\tAverage Score: -43.11\n",
      "Episode 69\tAverage Score: -43.33\n",
      "Episode 70\tAverage Score: -50.82\n",
      "Episode 71\tAverage Score: -50.92\n",
      "Episode 72\tAverage Score: -51.19\n",
      "Episode 73\tAverage Score: -51.52\n",
      "Episode 74\tAverage Score: -58.47\n",
      "Episode 75\tAverage Score: -65.41\n",
      "Episode 76\tAverage Score: -72.41\n",
      "Episode 77\tAverage Score: -72.49\n",
      "Episode 78\tAverage Score: -78.96\n",
      "Episode 79\tAverage Score: -78.89\n",
      "Episode 80\tAverage Score: -78.88\n",
      "Episode 81\tAverage Score: -78.94\n",
      "Episode 82\tAverage Score: -79.02\n",
      "Episode 83\tAverage Score: -78.95\n",
      "Episode 84\tAverage Score: -77.09\n",
      "Episode 85\tAverage Score: -76.35\n",
      "Episode 86\tAverage Score: -75.76\n",
      "Episode 87\tAverage Score: -75.19\n",
      "Episode 88\tAverage Score: -80.56\n",
      "Episode 89\tAverage Score: -80.51\n",
      "Episode 90\tAverage Score: -80.50\n",
      "Episode 91\tAverage Score: -79.98\n",
      "Episode 92\tAverage Score: -79.92\n",
      "Episode 93\tAverage Score: -85.35\n",
      "Episode 94\tAverage Score: -85.41\n",
      "Episode 95\tAverage Score: -85.18\n",
      "Episode 96\tAverage Score: -90.19\n",
      "Episode 97\tAverage Score: -89.75\n",
      "Episode 98\tAverage Score: -89.75\n",
      "Episode 99\tAverage Score: -94.66\n",
      "Episode 100\tAverage Score: -94.48\n",
      "Total training time: 00:07:00.18\n"
     ]
    }
   ],
   "source": [
    "from utility import score_save\n",
    "\n",
    "start = time.time()\n",
    "scores,avg_scores, agent = DQN(cfg)\n",
    "end = time.time()\n",
    "score_save(scores, averagescore=avg_scores, file_name=file_name, path=p_model, print_=False)\n",
    "hours, rem = divmod(end-start, 3600)\n",
    "minutes, seconds = divmod(rem, 60)\n",
    "print(\"Total training time: {:0>2}:{:0>2}:{:05.2f}\".format(int(hours),int(minutes),seconds))\n",
    "\n",
    "\n",
    "if p_model.is_dir():\n",
    "    nb_name = 'Run_log_training.txt'\n",
    "    with open(p_model.joinpath(nb_name), \"w\") as f:\n",
    "        f.write(\"Total training time: {:0>2}:{:0>2}:{:05.2f}\\n\".format(int(hours),int(minutes),seconds))\n",
    "        f.write('Toal epoch:%s\\n'%cfg.n_episodes)\n",
    "        f.write('Csv file name:%s\\n'%file_name)\n",
    "        f.close()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot training result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Episode = cfg.n_episodes\n",
    "image_name = f\"DQN_Scores{Episode}.png\"\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(np.arange(1, len(scores)+1), scores, label=\"Score of the episode\")\n",
    "plt.plot(np.arange(1, len(avg_scores)+1), avg_scores, label=\"Average score of the episode\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.ylabel('Score')\n",
    "plt.xlabel(f'Training Episode')\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "\n",
    "# plt.savefig((p_model/image_name))\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "DQN_win",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}