01_env.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "env_name = 'CartPole-v0'\n",
    "env = gym.make(env_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### env成员变量\n",
    "环境定义了动作空间和状态空间\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TimeLimit<OrderEnforcing<PassiveEnvChecker<CartPoleEnv<CartPole-v0>>>>>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discrete(2)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.action_space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.action_space.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.6194583e+00, -2.9336788e+38,  2.6264152e-01, -7.7780267e+37],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.observation_space\n",
    "env.observation_space.low\n",
    "env.observation_space.high\n",
    "env.observation_space.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### action和env交互实例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;31mSignature:\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mDocstring:\u001b[0m\n",
      "Steps through the environment and if the number of steps elapsed exceeds ``max_episode_steps`` then truncate.\n",
      "\n",
      "Args:\n",
      "    action: The environment step action\n",
      "\n",
      "Returns:\n",
      "    The environment step ``(observation, reward, terminated, truncated, info)`` with `truncated=True`\n",
      "    if the number of steps elapsed >= max episode steps\n",
      "\u001b[1;31mFile:\u001b[0m      e:\\anaconda3\\envs\\ai_lab\\lib\\site-packages\\gym\\wrappers\\time_limit.py\n",
      "\u001b[1;31mType:\u001b[0m      method"
     ]
    }
   ],
   "source": [
    "??env.step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total reward Score: 37.0\n"
     ]
    }
   ],
   "source": [
    "done = False\n",
    "score = 0\n",
    "state = env.reset()\n",
    "\n",
    "while not done:\n",
    "    env.render()\n",
    "    action = env.action_space.sample()\n",
    "    observation, reward, terminated, truncated, info = env.step(action)\n",
    "    score += reward\n",
    "    if terminated or truncated:\n",
    "        done = True\n",
    "        state = env.reset()\n",
    "        \n",
    "print('Total reward Score:', score)\n",
    "env.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Total reward Score: 11.0\n",
      "Epoch: 2, Total reward Score: 13.0\n",
      "Epoch: 3, Total reward Score: 17.0\n",
      "Epoch: 4, Total reward Score: 32.0\n",
      "Epoch: 5, Total reward Score: 16.0\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(1, 5+1):\n",
    "    done = False\n",
    "    score = 0\n",
    "    state = env.reset()\n",
    "    while not done:\n",
    "        env.render()\n",
    "        action = env.action_space.sample()\n",
    "        # 关键就在此处，后续可以引进神经网络来预测action！！！\n",
    "        # 这里我们每次只是随机取了一个action\n",
    "        observation, reward, terminated, truncated, info = env.step(action)\n",
    "        score += reward\n",
    "        if terminated or truncated:\n",
    "            done = True\n",
    "            state = env.reset()\n",
    "    print(f'Epoch: {epoch}, Total reward Score:', score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\anaconda3\\envs\\ai_lab\\lib\\site-packages\\gym\\envs\\registration.py:555: UserWarning: \u001b[33mWARN: The environment CartPole-v0 is out of date. You should consider upgrading to version `v1`.\u001b[0m\n",
      "  logger.warn(\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "import time\n",
    "\n",
    "env_name = 'CartPole-v0'\n",
    "env = gym.make(env_name, render_mode='rgb_array')\n",
    "state = env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total reward Score: 29.0\n"
     ]
    }
   ],
   "source": [
    "total_reward = 0\n",
    "done = False\n",
    "frames = []\n",
    "while not done:\n",
    "    frames.append(env.render())\n",
    "    action = env.action_space.sample()\n",
    "    observation, reward, terminated, truncated, info = env.step(action)\n",
    "    total_reward += reward\n",
    "    if terminated or truncated:\n",
    "        done = True\n",
    "        state = env.reset()\n",
    "    time.sleep(0.2)\n",
    "env.close()\n",
    "print('Total reward Score:', total_reward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29 (400, 600, 3)\n"
     ]
    }
   ],
   "source": [
    "print(len(frames),frames[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_frames2video(frames,dpi):\n",
    "    plt.figure(figsize=(frames[0].shape[0]/dpi, frames[0].shape[1]/dpi), dpi=dpi)\n",
    "    plt.axis('off')\n",
    "    patch = plt.imshow(frames[0])\n",
    "    def animate(frame):\n",
    "        patch.set_data(frame)\n",
    "    anim = animation.FuncAnimation(plt.gcf(), animate, frames = frames[1:], interval=50,repeat=False)\n",
    "    # anim.save('cartpole.mp4')\n",
    "    anim.save('cartpole.gif', writer='imagemagick')\n",
    "    HTML(anim.to_jshtml())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "MovieWriter imagemagick unavailable; using Pillow instead.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_frames2video(frames, dpi=100)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "ai_lab",
   "language": "python",
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