编写强化学习环境（Predator Game）-代码.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: opencv-python in /Users/liangsy/opt/anaconda3/lib/python3.8/site-packages (4.5.2.52)\r\n",
      "Requirement already satisfied: numpy>=1.17.3 in /Users/liangsy/opt/anaconda3/lib/python3.8/site-packages (from opencv-python) (1.19.5)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install opencv-python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pillow in /Users/liangsy/opt/anaconda3/lib/python3.8/site-packages (7.2.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "from PIL import Image\n",
    "import time\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "SIZE = 10\n",
    "EPISODES = 30000\n",
    "SHOW_EVERY = 3000\n",
    "\n",
    "FOOD_REWARD = 25\n",
    "ENEMY_PENALITY = 300\n",
    "MOVE_PENALITY = 1\n",
    "\n",
    "epsilon = 0.6\n",
    "EPS_DECAY = 0.9998\n",
    "DISCOUNT = 0.95\n",
    "LEARNING_RATE = 0.1\n",
    "\n",
    "q_table = 'qtable_1636204341.pickle'\n",
    "\n",
    "d = {1:(255,0,0), #blue\n",
    "     2:(0,255,0), #green\n",
    "     3:(0,0,255)} #red\n",
    "\n",
    "PLAYER_N = 1\n",
    "FOOD_N =2\n",
    "ENEMY_N =3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Cube:\n",
    "    def __init__(self):\n",
    "        self.x = np.random.randint(0, SIZE)\n",
    "        self.y = np.random.randint(0, SIZE)\n",
    "        \n",
    "    def __str__(self):\n",
    "        return f'{self.x},{self.y}'\n",
    "    \n",
    "    def __sub__(self,other):\n",
    "        return (self.x-other.x,self.y-other.y)\n",
    "    \n",
    "    def action(self,choise):\n",
    "        if choise == 0 :\n",
    "            self.move(x=1, y=1)\n",
    "        elif choise == 1 :\n",
    "            self.move(x=-1, y=1)\n",
    "        elif choise == 2 :\n",
    "            self.move(x=1, y=-1)\n",
    "        elif choise == 3 :\n",
    "            self.move(x=-1, y=-1)\n",
    "    \n",
    "    def move(self,x=False,y=False):\n",
    "        if not x:\n",
    "            self.x += np.random.randint(-1,2)\n",
    "        else:\n",
    "            self.x += x\n",
    "            \n",
    "        if not y:\n",
    "            self.y += np.random.randint(-1,2)\n",
    "        else:\n",
    "            self.y += y   \n",
    "            \n",
    "        if self.x < 0 :\n",
    "            self.x = 0\n",
    "        elif self.x >= SIZE :\n",
    "            self.x = SIZE -1\n",
    "\n",
    "        if self.y < 0 :\n",
    "            self.y = 0\n",
    "        elif self.y >= SIZE :\n",
    "            self.y = SIZE -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "if q_table is None:\n",
    "    q_table = {}\n",
    "    for x1 in range(-SIZE+1, SIZE):\n",
    "        for y1 in range(-SIZE+1, SIZE):\n",
    "            for x2 in range(-SIZE+1, SIZE):\n",
    "                for y2 in range(-SIZE+1, SIZE):\n",
    "                    q_table[((x1,y1),(x2,y2))] = [np.random.uniform(-5,0) for i in range(4)]\n",
    "else:\n",
    "    with open(q_table,'rb') as f:\n",
    "        q_table=pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode #0, epsilon:0.6\n",
      "mean reward:nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/liangsy/opt/anaconda3/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/Users/liangsy/opt/anaconda3/lib/python3.8/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode #3000, epsilon:0.32926722239570905\n",
      "mean reward:-32.624\n",
      "episode #6000, epsilon:0.18069483957364205\n",
      "mean reward:-12.621666666666666\n",
      "episode #9000, epsilon:0.09916147987941909\n",
      "mean reward:-9.519333333333334\n",
      "episode #12000, epsilon:0.054417708414240505\n",
      "mean reward:-9.794333333333332\n",
      "episode #15000, epsilon:0.029863279497827713\n",
      "mean reward:-8.415666666666667\n",
      "episode #18000, epsilon:0.016388331819794114\n",
      "mean reward:-6.570666666666667\n",
      "episode #21000, epsilon:0.00899356749667138\n",
      "mean reward:-4.325666666666667\n",
      "episode #24000, epsilon:0.00493547831509555\n",
      "mean reward:-3.7876666666666665\n",
      "episode #27000, epsilon:0.0027084853933429503\n",
      "mean reward:-2.073\n",
      "27001\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "episode_rewards = []\n",
    "for episode in range(EPISODES):\n",
    "    player = Cube()\n",
    "    food = Cube()\n",
    "    enemy = Cube()\n",
    "\n",
    "    if episode%SHOW_EVERY == 0 :\n",
    "        print(f'episode #{episode}, epsilon:{epsilon}')\n",
    "        print(f'mean reward:{np.mean(episode_rewards[-SHOW_EVERY:])}')\n",
    "        show = True\n",
    "    else:\n",
    "        show = False\n",
    "    \n",
    "    episode_reward = 0\n",
    "    for i in range(200):\n",
    "        obs = (player-food, player-enemy)\n",
    "        if np.random.random() > epsilon :\n",
    "            action = np.argmax(q_table[obs])\n",
    "        else:\n",
    "            action = np.random.randint(0,4)\n",
    "        \n",
    "        player.action(action)\n",
    "#         food.move()\n",
    "#         enemy.move()\n",
    "        \n",
    "        if player.x == food.x and player.y == food.y :\n",
    "            reward = FOOD_REWARD\n",
    "        elif player.x == enemy.x and player.y == enemy.y :\n",
    "            reward = -ENEMY_PENALITY\n",
    "        else:\n",
    "            reward = -MOVE_PENALITY\n",
    "            \n",
    "        \n",
    "        #Update the Q_table\n",
    "        current_q = q_table[obs][action]\n",
    "        new_obs =  (player-food, player-enemy)\n",
    "        max_future_q = np.max(q_table[new_obs])                \n",
    "        if reward == FOOD_REWARD:\n",
    "            new_q = FOOD_REWARD\n",
    "        else:\n",
    "            new_q = (1 - LEARNING_RATE)*current_q + LEARNING_RATE*(reward+DISCOUNT*max_future_q)        \n",
    "        q_table[obs][action] = new_q\n",
    "        \n",
    "        if show:\n",
    "            env = np.zeros((SIZE,SIZE,3), dtype=np.uint8)\n",
    "            env[food.x][food.y] = d[FOOD_N]\n",
    "            env[player.x][player.y] = d[PLAYER_N]\n",
    "            env[enemy.x][enemy.y] = d[ENEMY_N]\n",
    "            img = Image.fromarray(env,'RGB')\n",
    "            img = img.resize((800,800))\n",
    "            cv2.imshow('',np.array(img))\n",
    "            if reward == FOOD_REWARD or reward == -ENEMY_PENALITY:\n",
    "                if cv2.waitKey(500) & 0xFF == ord('q') :\n",
    "                    break\n",
    "            else:\n",
    "                if cv2.waitKey(1) & 0xFF == ord('q') :\n",
    "                    break\n",
    "                   \n",
    "        episode_reward += reward\n",
    "        \n",
    "        if reward == FOOD_REWARD or reward == -ENEMY_PENALITY:\n",
    "            break\n",
    "    \n",
    "    episode_rewards.append(episode_reward)\n",
    "    epsilon *= EPS_DECAY\n",
    "\n",
    "moving_avg = np.convolve(episode_rewards,np.ones((SHOW_EVERY,))/SHOW_EVERY,mode='valid')\n",
    "print(len(moving_avg))\n",
    "plt.plot([i for i in range(len(moving_avg))],moving_avg)\n",
    "plt.xlabel('episode #')\n",
    "plt.ylabel(f'mean {SHOW_EVERY} reward')\n",
    "plt.show()\n",
    "\n",
    "with open(f'qtable_{int(time.time())}.pickle','wb') as f:\n",
    "    pickle.dump(q_table,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(f'qtable_{int(time.time())}.pickle','wb') as f:\n",
    "    pickle.dump(q_table,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27001\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "moving_avg = np.convolve(episode_rewards,np.ones((SHOW_EVERY,))/SHOW_EVERY,mode='valid')\n",
    "print(len(moving_avg))\n",
    "plt.plot([i for i in range(len(moving_avg))],moving_avg)\n",
    "plt.xlabel('episode #')\n",
    "plt.ylabel(f'mean {SHOW_EVERY} reward')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.          9.         10.         14.33333333 26.         40.66666667\n",
      " 52.         53.33333333]\n"
     ]
    }
   ],
   "source": [
    "conv = np.convolve([3,8,10,9,11,23,44,55,57,48],np.ones((3,))/3,mode='valid')\n",
    "print(conv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([10,9,11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "food = Cube()\n",
    "player = Cube()\n",
    "enemy = Cube()\n",
    "\n",
    "env = np.zeros((SIZE,SIZE,3), dtype=np.uint8)\n",
    "env[food.x][food.y] = d[FOOD_N]\n",
    "env[player.x][player.y] = d[PLAYER_N]\n",
    "env[enemy.x][enemy.y] = d[ENEMY_N]\n",
    "\n",
    "img = Image.fromarray(env,'RGB')\n",
    "img = img.resize((800,800))\n",
    "\n",
    "cv2.imshow('',np.array(img))\n",
    "\n",
    "if cv2.waitKey(500) & 0xFF == ord('q') :\n",
    "    pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130321\n"
     ]
    }
   ],
   "source": [
    "print(len(q_table))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130321"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "19**4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.7773554618819225, -3.563705005054187, -2.2330000919778783, -2.4506302861658162]\n"
     ]
    }
   ],
   "source": [
    "print(q_table[((1,3),(-2,-4))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7,4\n",
      "8,5\n"
     ]
    }
   ],
   "source": [
    "player = Cube()\n",
    "print(player)\n",
    "player.action(0)\n",
    "print(player)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 255, 0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[FOOD_N]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}