{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "curr_path = str(Path().absolute())\n",
    "parent_path = str(Path().absolute().parent)\n",
    "sys.path.append(parent_path) # 添加路径到系统路径\n",
    "\n",
    "import gym\n",
    "import torch\n",
    "import math\n",
    "import datetime\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "from envs.gridworld_env import CliffWalkingWapper\n",
    "from QLearning.agent import QLearning\n",
    "from common.utils import plot_rewards\n",
    "from common.utils import save_results,make_dir\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QLearning算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLearning(object):\n",
    "    def __init__(self,n_states,\n",
    "                 n_actions,cfg):\n",
    "        self.n_actions = n_actions \n",
    "        self.lr = cfg.lr  # 学习率\n",
    "        self.gamma = cfg.gamma  \n",
    "        self.epsilon = 0 \n",
    "        self.sample_count = 0  \n",
    "        self.epsilon_start = cfg.epsilon_start\n",
    "        self.epsilon_end = cfg.epsilon_end\n",
    "        self.epsilon_decay = cfg.epsilon_decay\n",
    "        self.Q_table  = defaultdict(lambda: np.zeros(n_actions)) # 用嵌套字典存放状态->动作->状态-动作值（Q值）的映射，即Q表\n",
    "    def choose_action(self, state):\n",
    "        self.sample_count += 1\n",
    "        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \\\n",
    "            math.exp(-1. * self.sample_count / self.epsilon_decay) # epsilon是会递减的，这里选择指数递减\n",
    "        # e-greedy 策略\n",
    "        if np.random.uniform(0, 1) > self.epsilon:\n",
    "            action = np.argmax(self.Q_table[str(state)]) # 选择Q(s,a)最大对应的动作\n",
    "        else:\n",
    "            action = np.random.choice(self.n_actions) # 随机选择动作\n",
    "        return action\n",
    "    def predict(self,state):\n",
    "        action = np.argmax(self.Q_table[str(state)])\n",
    "        return action\n",
    "    def update(self, state, action, reward, next_state, done):\n",
    "        Q_predict = self.Q_table[str(state)][action] \n",
    "        if done: # 终止状态\n",
    "            Q_target = reward  \n",
    "        else:\n",
    "            Q_target = reward + self.gamma * np.max(self.Q_table[str(next_state)]) \n",
    "        self.Q_table[str(state)][action] += self.lr * (Q_target - Q_predict)\n",
    "    def save(self,path):\n",
    "        import dill\n",
    "        torch.save(\n",
    "            obj=self.Q_table,\n",
    "            f=path+\"Qleaning_model.pkl\",\n",
    "            pickle_module=dill\n",
    "        )\n",
    "        print(\"保存模型成功！\")\n",
    "    def load(self, path):\n",
    "        import dill\n",
    "        self.Q_table =torch.load(f=path+'Qleaning_model.pkl',pickle_module=dill)\n",
    "        print(\"加载模型成功！\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(cfg,env,agent):\n",
    "    print('开始训练!')\n",
    "    print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')\n",
    "    rewards = []  # 记录奖励\n",
    "    ma_rewards = [] # 记录滑动平均奖励\n",
    "    for i_ep in range(cfg.train_eps):\n",
    "        ep_reward = 0  # 记录每个episode的reward\n",
    "        state = env.reset()  # 重置环境, 重新开一局（即开始新的一个episode）\n",
    "        while True:\n",
    "            action = agent.choose_action(state)  # 根据算法选择一个动作\n",
    "            next_state, reward, done, _ = env.step(action)  # 与环境进行一次动作交互\n",
    "            agent.update(state, action, reward, next_state, done)  # Q-learning算法更新\n",
    "            state = next_state  # 存储上一个观察值\n",
    "            ep_reward += reward\n",
    "            if done:\n",
    "                break\n",
    "        rewards.append(ep_reward)\n",
    "        if ma_rewards:\n",
    "            ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)\n",
    "        else:\n",
    "            ma_rewards.append(ep_reward)\n",
    "        if (i_ep+1)%20 == 0: \n",
    "            print('回合：{}/{}, 奖励：{}'.format(i_ep+1, cfg.train_eps, ep_reward))\n",
    "    print('完成训练！')\n",
    "    return rewards,ma_rewards"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(cfg,env,agent):\n",
    "    # env = gym.make(\"FrozenLake-v0\", is_slippery=False)  # 0 left, 1 down, 2 right, 3 up\n",
    "    # env = FrozenLakeWapper(env)\n",
    "    print('开始测试!')\n",
    "    print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')\n",
    "    # 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0\n",
    "    cfg.epsilon_start = 0.0 # e-greedy策略中初始epsilon\n",
    "    cfg.epsilon_end = 0.0 # e-greedy策略中的终止epsilon\n",
    "    rewards = [] # 记录所有回合的奖励\n",
    "    ma_rewards = []  # 记录所有回合的滑动平均奖励\n",
    "    rewards = []  # 记录所有episode的reward\n",
    "    ma_rewards = [] # 滑动平均的reward\n",
    "    for i_ep in range(cfg.test_eps):\n",
    "        ep_reward = 0  # 记录每个episode的reward\n",
    "        state = env.reset()  # 重置环境, 重新开一局（即开始新的一个episode）\n",
    "        while True:\n",
    "            action = agent.predict(state)  # 根据算法选择一个动作\n",
    "            next_state, reward, done, _ = env.step(action)  # 与环境进行一个交互\n",
    "            state = next_state  # 存储上一个观察值\n",
    "            ep_reward += reward\n",
    "            if done:\n",
    "                break\n",
    "        rewards.append(ep_reward)\n",
    "        if ma_rewards:\n",
    "            ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)\n",
    "        else:\n",
    "            ma_rewards.append(ep_reward)\n",
    "        print(f\"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}\")\n",
    "    print('完成测试！')\n",
    "    return rewards,ma_rewards"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 设置参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "curr_time = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") # 获取当前时间\n",
    "algo_name = 'Q-learning'  # 算法名称\n",
    "env_name = 'CliffWalking-v0'  # 环境名称\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") # 检测GPU\n",
    "class QlearningConfig:\n",
    "    '''训练相关参数'''\n",
    "    def __init__(self):\n",
    "        self.algo_name = algo_name # 算法名称\n",
    "        self.env_name = env_name # 环境名称\n",
    "        self.device = device # 检测GPU\n",
    "        self.train_eps = 400 # 训练的回合数\n",
    "        self.test_eps = 20 # 测试的回合数\n",
    "        self.gamma = 0.9 # reward的衰减率\n",
    "        self.epsilon_start = 0.95 # e-greedy策略中初始epsilon\n",
    "        self.epsilon_end = 0.01 # e-greedy策略中的终止epsilon\n",
    "        self.epsilon_decay = 300 # e-greedy策略中epsilon的衰减率\n",
    "        self.lr = 0.1 # 学习率      \n",
    "class PlotConfig:\n",
    "    ''' 绘图相关参数设置\n",
    "    '''\n",
    "\n",
    "    def __init__(self) -> None:\n",
    "        self.algo_name = algo_name  # 算法名称\n",
    "        self.env_name = env_name  # 环境名称\n",
    "        self.device = device # 检测GPU\n",
    "        self.result_path = curr_path + \"/outputs/\" + self.env_name + \\\n",
    "            '/' + curr_time + '/results/'  # 保存结果的路径\n",
    "        self.model_path = curr_path + \"/outputs/\" + self.env_name + \\\n",
    "            '/' + curr_time + '/models/'  # 保存模型的路径\n",
    "        self.save = True  # 是否保存图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建环境和智能体"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def env_agent_config(cfg,seed=1):\n",
    "    '''创建环境和智能体\n",
    "    Args:\n",
    "        cfg ([type]): [description]\n",
    "        seed (int, optional): 随机种子. Defaults to 1.\n",
    "    Returns:\n",
    "        env [type]: 环境\n",
    "        agent : 智能体\n",
    "    '''    \n",
    "    env = gym.make(cfg.env_name)  \n",
    "    env = CliffWalkingWapper(env)\n",
    "    env.seed(seed) # 设置随机种子\n",
    "    n_states = env.observation_space.n # 状态维度\n",
    "    n_actions = env.action_space.n # 动作维度\n",
    "    agent = QLearning(n_states,n_actions,cfg)\n",
    "    return env,agent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 执行训练并输出结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练!\n",
      "环境：CliffWalking-v0, 算法：Q-learning, 设备：cuda\n",
      "回合：20/400, 奖励：-82\n",
      "回合：40/400, 奖励：-51\n",
      "回合：60/400, 奖励：-50\n",
      "回合：80/400, 奖励：-53\n",
      "回合：100/400, 奖励：-21\n",
      "回合：120/400, 奖励：-35\n",
      "回合：140/400, 奖励：-44\n",
      "回合：160/400, 奖励：-28\n",
      "回合：180/400, 奖励：-28\n",
      "回合：200/400, 奖励：-17\n",
      "回合：220/400, 奖励：-18\n",
      "回合：240/400, 奖励：-22\n",
      "回合：260/400, 奖励：-19\n",
      "回合：280/400, 奖励：-15\n",
      "回合：300/400, 奖励：-14\n",
      "回合：320/400, 奖励：-13\n",
      "回合：340/400, 奖励：-13\n",
      "回合：360/400, 奖励：-13\n",
      "回合：380/400, 奖励：-13\n",
      "回合：400/400, 奖励：-13\n",
      "完成训练！\n",
      "保存模型成功！\n",
      "结果保存完毕！\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "加载模型成功！\n",
      "开始测试!\n",
      "环境：CliffWalking-v0, 算法：Q-learning, 设备：cuda\n",
      "回合：1/20，奖励：-13.0\n",
      "回合：2/20，奖励：-13.0\n",
      "回合：3/20，奖励：-13.0\n",
      "回合：4/20，奖励：-13.0\n",
      "回合：5/20，奖励：-13.0\n",
      "回合：6/20，奖励：-13.0\n",
      "回合：7/20，奖励：-13.0\n",
      "回合：8/20，奖励：-13.0\n",
      "回合：9/20，奖励：-13.0\n",
      "回合：10/20，奖励：-13.0\n",
      "回合：11/20，奖励：-13.0\n",
      "回合：12/20，奖励：-13.0\n",
      "回合：13/20，奖励：-13.0\n",
      "回合：14/20，奖励：-13.0\n",
      "回合：15/20，奖励：-13.0\n",
      "回合：16/20，奖励：-13.0\n",
      "回合：17/20，奖励：-13.0\n",
      "回合：18/20，奖励：-13.0\n",
      "回合：19/20，奖励：-13.0\n",
      "回合：20/20，奖励：-13.0\n",
      "完成测试！\n",
      "结果保存完毕！\n"
     ]
    },
    {
     "data": {
      "image/png": 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jMHLkSPTt2xft27cHgGJ/L7906VKYmZnBx8cHXbt2xaZNmwA8fsOnTJmC0aNHo1+/foYTk6Xx8/PDqVOn0LlzZ9SvX9/weknb61ldu3bFe++9h2nTpqFbt25IS0tDZGRkeTaDwZgxY+Dj44OxY8eiQ4cOCAsLM/yCZsGCBVi3bh3c3d2RmpoKlUplmG/o0KHo1q0b/P39ERgYiH79+hmmWVhYYN68eZg+fTrc3NwQFxdX4rHbwYMHY+DAgQgKCkKfPn2gVCrxwQcflHkdPvzwQ/zjH//AP//5T7Rv3x5+fn5wcHDAqlWrynxytaaM12dNnjwZbdq0wcCBAzFw4EC88cYbhl/ilUW9evWwbds2mJmZYejQoVCpVBg1ahQsLS3x6quvVmidnuXm5oasrCyjz5CrqyuysrLg5uZmeO2jjz7CihUroFKpsHLlSvj4+JS6bKVSiS+//BJZWVmYO3duuUPis88+Q3JyMtzd3bFs2TIMGDCgxG2vVCrRt29fJCUlGR0hAIAvvvgC//nPf+Dm5obPPvsMK1asMPobIEbGGwZVr2vXrsHPzw8XL14s0zdWev6tWLEC8fHx2Lp1K15++eXqLqdScbxWr+nTp6N58+YIDg6ukv5q7B7E8+zJieJ79+7h008/Ra9evfhhq0GCg4Px5ptvIjk5ubpLqRQcr9XnwoUL+PPPP6HX65GYmIiEhIRi/0e/KXAPohqMGzcOycnJUCgUcHNzw0cffWR04o7oecLxWn1+/PFHzJ8/H3fv3kWjRo3wzjvvYPDgwVXWPwOCiIhE8RATERGJYkAQEZEoBgQREYmqUT9FyM7Og15fsVMqDRpYICsrt/SG1YT1ScP6pGF90jyv9cnlMlhbF3+5jRoVEHq9UOGAeDL/84z1ScP6pGF90jzv9YnhISYiIhLFgCAiIlE16hATEVUfQRCQnX0LWu1DAFV7OCUzU26yK75WhuquT6Ewg4VFPdSpU/LlvZ/FgCCiSpGbew8ymQx2dk0gk1XtwQkzMzkePXp+A6I66xMEAYWFWty9+/hy+eUJCR5iIqJK8eBBLiwt61V5OFDJZDIZlMpaqFfPBrm5d8s1L99JIqoUer0OCgUPSjyvzM2V0OlKv43u0xgQRFRpSrtDHFWfirw3DAgiIhMLD/8I3323o7rLKDcGBBHVeI8ele/Qyt+lL1PjAUMiqpG6dXPFmDETcPz4z3B374K33hqBqKhIXLt2FVqtFiqVK6ZNm4EbN9Iwd+4sbNmyE48ePYKvbx+MGjUOb701EgkJ8Th69Cd8/PEn2LZtCxISfoBO9whKZS2EhITi9ddbifYVEDAYCxd+hKys22jUyB4Kxf++i8fE7MLOnd/A3FwJQdAjPHwJXn31tWrZRqVhQBBRpfv54k0cu3DTJMvu1s4eHm3ty9S2Vq1aWLt2MwBgyZIFcHHpgNDQD6DX6zF//jzs27cXAwcGIj8/D7dv30Z6ugbNmrXA6dOn8NZbI3HmzEm4uj6+L7W3ty+GDw8CAJw6dQKffroY0dEbRfsKC/sn2rdXYezYd3Djxl8YM+YtdOrUBQCwatVybN36HRo2bAitVvtc//+NSgmImJgYrF27FteuXcPcuXMRFBRkmDZ//nwcP34cSqUSL730EsLCwtC2bdtil3Xnzh34+fnB1dUVK1asqIzyiOgF5ePjZ3h87FgiUlIuYfv2rQCAhw8fwtbWDgDQoYMrzpw5iZs3NfD3H4StWzejsLAQp0+fRFDQaADAlSsp+PrrDbh//x7kcjnS0v4stq+zZ89g+vR/AgAaN24CV9dOhmkdOrjhk08+godHd3Tp0g2NGzcxybpXhkoJCCcnJ0RGRiI6OrrINE9PT8ydOxfm5uY4fPgwZsyYgUOHDhW7rI8//hg9evRAXl5eZZRGRNXAo23Zv+WbUp06Lz31TMCiRZ+J/kHu2NENZ86cgkZzAx9+uADJyWdx6NBBCALg4NAYhYWF+OCD2fjyy6/QqlVr3L59CwEBPiX0VbxFiz5FSsolnDlzGsHBExESMgddunhIWU2TqZST1C1btoSjoyPk8qKL69WrF8zNzQEALi4uSE9PL3aXau/evWjYsCHc3NwqoywiIgMPD09s2bIJOp0OAHD37l1oNDcAPA6IEyeOIycnB7a2dnB17YR16/5lOLyk1RZAp9MZ9jh27fq2xL46dnTFvn17AQAazQ2cPn0SwOMT2BrNDTg7t8GIEaPRqVNnXL16xSTrWxmq9BzE1q1b0bNnT9EgycjIwMaNG/H111/j4MGDFVp+gwYWkuqzsbGUNL+psT5pWJ80pdWXmSmHmVn1/TBSrG8zs//V9P77/8SXXy7HmDFvQSaTwdzcHNOnh+CVV5rCwcEedevWhYuLC8zM5OjUyR3h4R/Aza0TzMzksLJ6GRMmTMSECSNhZWWF3r29ivRp3NcshId/gKCgg3BwaAyVqiPkchnkcmDRoo+Rm5v738uS2GHq1OAq225yubxc40wmCEKpV9UKDAyERqMRnZaUlASFQgEACA0NRZs2bYzOQTyxb98+rFixAlu3bkXDhg2LTH/nnXcwZswYdOnSBbt27cJPP/1U7nMQWVm5Fb7muo2NJW7dyqnQvFWB9UnD+qQpS33p6X+gUaNXq6giY7wWU9k8+x7J5bISv1iXaQ9i9+7dkoqKj49HZGQkNm7cKBoOAJCcnIywsDAAQF5eHgoKCjBhwgR89dVXkvomIqKKMfkhpsOHD2Px4sXYsGEDmjQp/mz9yZMnDY8rugdBRESVp1IOfMXFxcHT0xPff/89li9fDk9PT6SmpgIA5syZg8LCQgQHB8Pf3x/+/v7Izs4GAISFhSEhIaEySiAiokpWpnMQfxc8B1F9WJ80NaE+noMo3vNSX3nPQfBaTEREJIoBQUREohgQREQkigFBRPQ39MknH5v8HhMMCCKiCngR7jHBy30TUaUr/L+fUXgl0STLNm/lCfOWpV/crls3V0yYMAlHjx7BvXv3MHt2GE6fPokTJ5Lw6NEjLFgQgddea4asrNv4+OMw5OXlQavVomtXD0ye/F6xy6zIPSb69/fCqFFjK/UeE09fsshU95hgQBBRjWVhYYm1azfjxx8PYc6cmfj440WYOHEqtm7dhM2b1+PDDxfAwsISERGReOmll/Do0SO8//5U/PJLEjp37iq6zIrcY6J58+aVfo+J0aPfgru7ae8xwYAgokpn3tKjTN/yTa1Pn34AgFatWgOQwcOj+3+fO+HIkcMAAL1ej1WrluPixQsABGRlZeHq1f8rNiAqco+JgIDB+PrrTZV8j4n/XfXaVPeYYEAQUY2lVCoBPL6KqVJpbnhdLpcbLvu9Y8dW5OTcR3T0RtSqVQsREZ9Aqy0odpkVucdEePgnOHv2zN/uHhM8SU1EL7ScnBw0aNAQtWrVwq1bmTh27EiZ563ee0ycAmDae0xwD4KIXmhDhgzDBx/MxogRQ2FjY4eOHct+w7L33puJVatWYPTo4f+9x4QSwcEz4eDQGLa2dnjppZfQrp0LgMeBkZGRjg4dXAEAdetaYNy4dzFhwki8/LIVevXqU0pfIVi48CMcOnQQ9vYOUKk6Anh8iOyTTz5Gbm4OZDI57OzsMHHi1IptjGfwWkz/VROuhVOdWJ80NaE+XoupeM9LfbwWExERVQoGBBERiWJAEFGlqUFHrGucirw3DAgiqhRyuQI6XfVcEoJKV1iohUJRvt8lMSCIqFLUqWOBnJy7EITqPxlL/yMIArTaAty9ewsWFvXKNS9/5kpElcLCwgrZ2beQkfEXgKo91CSXyyvt8hKmUN31KRRmsLS0Rp06dcs1HwOCiCqFTCZD/fq21dJ3TfiZ8POIh5iIiEgUA4KIiERJDoiYmBio1Wo4Oztjy5YtRtPmz58Pb29vDBw4EMOGDcPFixeLXc7x48cxaNAg+Pr6wtfXF5cvX5ZaGhERSSD5HISTkxMiIyMRHR1dZJqnpyfmzp0Lc3NzHD58GDNmzMChQ4eKtMvIyEBYWBjWrl2L5s2b4+HDh9V2ByUiInpMckC0bNkSAIzubvREr169DI9dXFyQnp4OvV5fpO0333wDf39/NG/eHABQu3ZtqWUREZFEVfYrpq1bt6Jnz56iQZKamorGjRtj5MiRuH//Ptzd3TFz5kzDtdyJiKjqlRoQgYGB0Gg0otOSkpKgUChK7WTfvn2IjY3F1q1bRafrdDqcPXsWGzZsQK1atRASEoLo6GhMnVq+S9aWdFXCsrCxsZQ0v6mxPmlYnzSsT5rnvT4xpQbE7t27JXUQHx+PyMhIbNy4EQ0bNhRt4+DggDZt2sDS8vEG9Pb2RkxMTLn74uW+qw/rk4b1ScP6KqZaL/d9+PBhLF68GOvWrUOTJsXfI9XPzw8nTpyAVquFIAg4duwYWrdubcrSiIioFJIDIi4uDp6envj++++xfPlyeHp6IjU1FQAwZ84cFBYWIjg4GP7+/vD390d2djYAICwsDAkJCQCADh06oHv37ggICMDAgQOh0+nw7rvvSi2NiIgk4B3l/ut53QV8gvVJw/qkYX3SPK/18Y5yRERUIQwIIiISxYAgIiJRDAgiIhLFgCAiIlEMCCIiEsWAICIiUQwIIiISxYAgIiJRDAgiIhLFgCAiIlEMCCIiEsWAICIiUQwIIiISxYAgIiJRDAgiIhLFgCAiIlEMCCIiEsWAICIiUQwIIiISxYAgIiJRDAgiIhIlOSBiYmKgVqvh7OyMLVu2GE2bP38+vL29MXDgQAwbNgwXL14UXcaDBw8wc+ZM+Pn5wdfXF9OnT0dubq7U0oiISALJAeHk5ITIyEj4+fkVmebp6YnY2Fjs3bsX7777LmbMmCG6jB07dqCwsBCxsbGIi4uDXq/Htm3bpJZGREQSmEldQMuWLQEAcnnRrOnVq5fhsYuLC9LT06HX64u0lclkePjwIQoLCwEA+fn5aNSokdTSiIhIAskBUVZbt25Fz549RYNk2LBhSE5OhoeHBwCgW7duUKvV5e6jQQMLSTXa2FhKmt/UWJ80rE8a1ifN816fmFIDIjAwEBqNRnRaUlISFApFqZ3s27cPsbGx2Lp1a7HLAYBjx44BAGbOnIl169Zh3LhxpS77aVlZudDrhXLN84SNjSVu3cqp0LxVgfVJw/qkYX3SPK/1yeWyEr9YlxoQu3fvllRAfHw8IiMjsXHjRjRs2FC0zfbt2+Hv749atWoBAAYMGIA9e/aUOyCIiKjymPRnrocPH8bixYuxbt06NGnSpNh2TZo0wbFjxyAIAvR6PY4ePYrXX3/dlKUREVEpJAdEXFwcPD098f3332P58uXw9PREamoqAGDOnDkoLCxEcHAw/P394e/vj+zsbABAWFgYEhISAABTpkzB/fv34efnB7VaDa1Wi0mTJkktjYiIJJAJglCxg/bPIZ6DqD6sTxrWJw3rq5jSzkHwf1ITEZEoBgQREYliQBARkSgGBBERiWJAEBGRKAYEERGJYkAQEZEoBgQREYliQBARkSgGBBERiWJAEBGRKAYEERGJYkAQEZEoBgQREYliQBARkSgGBBERiWJAEBGRKAYEERGJYkAQEZEoBgQREYliQBARkSjJARETEwO1Wg1nZ2ds2bLFaNrq1auhVqsREBAAf39/7N+/v9jl7Ny5E3379oWXlxfCw8Oh1+ullkZERBKYSV2Ak5MTIiMjER0dXWRaUFAQJk2aBADIyMiAj48PPDw8YGVlZdQuLS0NX375Jfbs2YN69ephwoQJ2Lt3LwICAqSWR0REFSR5D6Jly5ZwdHSEXF50UZaWlobH+fn5kMlkonsGBw8ehJeXF+rXrw+5XI4hQ4aUuLdBRESmJ3kPojTbtm3Dpk2bkJ6ejkWLFsHa2rpIm5s3b8LBwcHw3MHBATdv3jR1aQaXftwHs+vHIeiFKuuzvK7IZaxPAtYnDeuTxtT16Zp3xRu9fSt9uaUGRGBgIDQajei0pKQkKBSKEucfPnw4hg8fjitXriAkJARdunQRDYnK0KCBRYXmq1PbHIUAZHJZ5RZUyVifNKxPGtYnjSnrq1PbHDY2lqU3LKdSA2L37t2V0lGrVq1ga2uLkydPon///kbT7O3tjUJIo9HA3t6+3H1kZeVCX4GUbt61H2z8B+PWrZxyz1tVbGwsWZ8ErE8a1idNVdRXkeXL5bISv1ib9GeuqamphsdpaWlISUmBo6NjkXb9+/fHoUOHcOfOHej1enz77bfw8fExZWlERFQKyecg4uLisHTpUty/fx8JCQmIjo7G+vXr4ejoiKioKKSmpsLMzAwKhQLz5s1DixYtAADLly+Hra0thg8fjqZNm2Ly5MkYOnQoAMDDwwMDBw6UWhoREUkgEwTh+T2zU04VPcQEcBdVKtYnDeuThvVVTLUeYiIior8vBgQREYliQBARkSgGBBERiWJAEBGRKAYEERGJYkAQEZEoBgQREYliQBARkSgGBBERiWJAEBGRKAYEERGJYkAQEZEoBgQREYliQBARkSgGBBERiWJAEBGRKAYEERGJYkAQEZEoBgQREYliQBARkSjJARETEwO1Wg1nZ2ds2bLFaNrq1auhVqsREBAAf39/7N+/X3QZhw4dwqBBg+Dn5wdfX1+sX79eallERCSRmdQFODk5ITIyEtHR0UWmBQUFYdKkSQCAjIwM+Pj4wMPDA1ZWVkbtbGxssHr1atjZ2SEnJweDBg1Cu3bt4OrqKrU8IiKqIMkB0bJlSwCAXF50Z8TS0tLwOD8/HzKZDHq9vki79u3bG83TokUL3LhxgwFBRFSNTH4OYtu2bfD29kZgYCAWLFgAa2vrEttfu3YNycnJ6Ny5s6lLIyKiEsgEQRBKahAYGAiNRiM6LSkpCQqFAgAQGhqKNm3aICgoSLTtlStXEBISgs2bNxcbEpmZmRgxYgSmT58OHx+f8qwHERFVslIPMe3evbtSOmrVqhVsbW1x8uRJ9O/fv8j0rKwsjBkzBuPHj69wOGRl5UKvLzHvimVjY4lbt3IqNG9VYH3SsD5pWJ80z2t9crkMDRpYFD/dlJ2npqYaHqelpSElJQWOjo5F2mVnZ2PMmDF4++23MWTIEFOWREREZST5JHVcXByWLl2K+/fvIyEhAdHR0Vi/fj0cHR0RFRWF1NRUmJmZQaFQYN68eWjRogUAYPny5bC1tcXw4cMRHR2N69evY8eOHdixYwcAYOTIkRg8eLDU8oiIqIJKPQfxd8JDTNWH9UnD+qRhfRVTrYeYiIjo74sBQUREohgQREQkigFBRESiGBBERCSKAUFERKIYEEREJIoBQUREohgQREQkigFBRESiGBBERCSKAUFERKIYEEREJIoBQUREohgQREQkigFBRESiGBBERCSKAUFERKIYEEREJIoBQUREohgQREQkigFBRESiJAdETEwM1Go1nJ2dsWXLFqNpq1evhlqtRkBAAPz9/bF///4Sl1VQUABfX18MGjRIallERCSRmdQFODk5ITIyEtHR0UWmBQUFYdKkSQCAjIwM+Pj4wMPDA1ZWVqLLioyMRPv27XH58mWpZRERkUSS9yBatmwJR0dHyOVFF2VpaWl4nJ+fD5lMBr1eL7qc06dP4/r16/D395daEhERVQLJexCl2bZtGzZt2oT09HQsWrQI1tbWRdrk5+dj0aJFWL16Na5fv17hvho0sJBQKWBjY1l6o2rE+qRhfdKwPmme9/rElBoQgYGB0Gg0otOSkpKgUChKnH/48OEYPnw4rly5gpCQEHTp0qVISCxduhRvvfUW7OzsJAVEVlYu9HqhQvPa2Fji1q2cCvdtaqxPGtYnDeuT5nmtTy6XlfjFutSA2L17d6UU0qpVK9ja2uLkyZPo37+/0bQzZ84gMTERq1atQkFBAe7duwe1Wo3Y2NhK6ZuIiMrPpIeYUlNT4ejoCABIS0tDSkqK4fnTng6CEydOICIiArt27TJlaUREVArJAREXF4elS5fi/v37SEhIQHR0NNavXw9HR0dERUUhNTUVZmZmUCgUmDdvHlq0aAEAWL58OWxtbTF8+HDJK0FERJVPJghCxQ7aP4d4DqL6sD5pWJ80rK9iSjsHwf9JTUREohgQREQkigFBRESiGBBERCSKAUFERKIYEEREJIoBQUREohgQREQkigFBRESiGBBERCSKAUFERKIYEEREJIoBQUREohgQREQkigFBRESiGBBERCSKAUFERKIYEEREJIoBQUREohgQREQkigFBRESiJAdETEwM1Go1nJ2dsWXLFqNpq1evhlqtRkBAAPz9/bF///5il5OSkoK3334bAwYMwIABA3DkyBGppRERkQRmUhfg5OSEyMhIREdHF5kWFBSESZMmAQAyMjLg4+MDDw8PWFlZGbXLz8/H1KlT8fnnn8PFxQWPHj1CTk6O1NKIiEgCyQHRsmVLAIBcXnRnxNLS0vA4Pz8fMpkMer2+SLu4uDh07NgRLi4uj4syM4O1tbXU0oiISALJAVGabdu2YdOmTUhPT8eiRYtE//CnpqbCzMwMEyZMQGZmJt544w3Mnj27yJ4GERFVHZkgCEJJDQIDA6HRaESnJSUlQaFQAABCQ0PRpk0bBAUFiba9cuUKQkJCsHnz5iIhsXDhQvz444/Yvn07GjZsiMWLFyM3NxeLFy+uyDoREVElKHUPYvfu3ZXSUatWrWBra4uTJ0+if//+RtPs7e3h7u4OW1tbAIBarcbcuXPL3UdWVi70+hLzrlg2Npa4dev5Pe/B+qRhfdKwPmme1/rkchkaNLAofropO09NTTU8TktLQ0pKChwdHYu08/HxwYULF5CbmwsASExMRKtWrUxZGhERlULyOYi4uDgsXboU9+/fR0JCAqKjo7F+/Xo4OjoiKirKcH5BoVBg3rx5aNGiBQBg+fLlsLW1xfDhw+Hg4IAJEyZg2LBhkMlkaNKkCRYsWCB55YiIqOJKPQfxd8JDTNWH9UnD+qRhfRVTrYeYiIjo74sBQUREohgQREQkigFBRESiGBBERCSKAUFERKIYEEREJIoBQUREohgQREQkigFBRESiGBBERCTK5DcMqkpyuaxa5zc11icN65OG9UnzPNZXWk016mJ9RERUeXiIiYiIRDEgiIhIFAOCiIhEMSCIiEgUA4KIiEQxIIiISBQDgoiIRDEgiIhIFAOCiIhE1ahLbZTm999/R2hoKO7evYt69eohIiICr732mlEbnU6HhQsX4ujRo5DJZHjnnXcwZMgQk9eWnZ2NWbNm4c8//4RSqcSrr76K8PBw1K9f36hdaGgokpKSYG1tDQDw9vbGpEmTTF4fAPTu3RtKpRK1atUCAISEhKB79+5GbR48eIA5c+bg0qVLUCgUmD17Nnr16mXy2v766y9MmTLF8DwnJwe5ubk4efKkUbuoqCh88803sLW1BQB06NABH330kUlqioiIwMGDB3Hjxg3ExsaiZcuWAMo2DgHTj0Wx+so6DgHTj8Xitl9ZxiFg+rEoVl9ZxyFQtWOxwoQXyIgRI4Q9e/YIgiAIe/bsEUaMGFGkze7du4WxY8cKOp1OyMrKErp37y6kpaWZvLbs7Gzhl19+MTxfsmSJMGfOnCLtZs+eLXz99dcmr0dMr169hCtXrpTYJioqSggLCxMEQRB+//13oWvXrkJubm5VlGdk4cKFwvz584u8vmLFCmHJkiVVUsOpU6cEjUZTZLuVZRwKgunHolh9ZR2HgmD6sVjc9ivLOBQE04/F4up7WnHjUBCqdixW1AtziCkrKwu//vor/Pz8AAB+fn749ddfcefOHaN2+/fvx5AhQyCXy1G/fn14eXnh+++/N3l99erVg7u7u+G5i4sLNBqNyfutbAcOHMCbb74JAHjttdfQpk0bJCYmVmkNWq0WsbGxGDx4cJX2+yxXV1fY29sbvVbWcQiYfiyK1fc8jUOx+srD1GOxtPqel3EoxQsTEDdv3oSdnR0UCgUAQKFQwNbWFjdv3izSzsHBwfDc3t4e6enpVVqrXq/Htm3b0Lt3b9HpGzZsgFqtxuTJk3Ht2rUqrS0kJARqtRoff/wx7t+/X2S6RqNB48aNDc+rY/v9+OOPsLOzwxtvvCE6fd++fVCr1Rg7dizOnTtXpbWVdRw+aVudY7G0cQhU31gsbRwC1T8WSxuHQPWOxbJ4YQLi72TBggV46aWXEBQUVGTajBkzEB8fj9jYWPTr1w/jx4+HTqerkrq2bt2KvXv34rvvvoMgCAgPD6+Sfsvru+++K/Zb27Bhw5CQkIDY2FiMGzcOkydPRnZ2dhVX+PdQ0jgEqm8s1oRxCPw9xuILExD29vbIyMgwDGCdTofMzMwiu4j29vZGu9Q3b95Eo0aNqqzOiIgI/PHHH1i2bBnk8qJvj52dneH1gIAA5OfnV9m3oifbSqlU4q233sLZs2eLtHFwcMCNGzcMz6t6+2VkZODUqVNQq9Wi021sbGBubg4A8PDwgL29Pa5evVpl9ZV1HD5pW11jsbRxCFTfWCzLOASqdyyWNg6B6h+LZfHCBESDBg3g5OSEuLg4AEBcXBycnJyK/DrD29sb3377LfR6Pe7cuYNDhw6hf//+VVLjF198gf/85z9YuXIllEqlaJuMjAzD46NHj0Iul8POzs7kteXn5yMnJwcAIAgC9u/fDycnpyLtvL29sWPHDgDA9evXcfHiRdFfmJjK7t270aNHD8Mva5719PZLSUnBjRs30KxZs6oqr8zjEKi+sViWcQhUz1gs6zgEqncsljYOgeofi2XxQt0w6Nq1awgNDcX9+/fx8ssvIyIiAs2bN8eECRMQHByMtm3bQqfTITw8HD///DMAYMKECYYTXaZ09epV+Pn54bXXXkPt2rUBAE2aNMHKlSvh7++P6Oho2NnZYfTo0cjKyoJMJoOFhQVmzZoFFxcXk9eXlpaGadOmQafTQa/Xo0WLFpg3bx5sbW2N6svPz0doaChSUlIgl8vxz3/+E15eXiav74n+/fsjLCwMnp6ehteefn9nz56NS5cuQS6Xw9zcHMHBwejRo4dJalm4cCF++OEH3L59G9bW1qhXrx727dtX7Dh8tlZTj0Wx+pYtW1bsOARQpWNRrL41a9YUOw6frc/UY7G49xcQH4dA9Y3FinqhAoKIiMruhTnERERE5cOAICIiUQwIIiISxYAgIiJRDAgiIhLFgCCqZGvWrEFYWFiF5g0NDUVkZGQlV0RUMS/U5b6JqsLEiROruwSiSsE9CCIiEsWAoBdeRkYGpk2bhs6dO6N3797YvHkzgMc3dAkODsb06dOhUqkQGBiIy5cvG+aLjo5G9+7doVKp0L9/fxw/ftwwX0hIiKFdQkICfH194erqihEjRhhd9fTXX39FYGAgVCoVpk+fjoKCAqPaDh8+DH9/f7i6umLYsGFl6p+o0lTfrSiIqp9OpxMCAwOFqKgooaCgQPjzzz+F3r17C4mJicKKFSsEZ2dn4cCBA4JWqxXWrl0r9OrVS9BqtcK1a9cET09PIT09XRAEQUhLSxP++OMPQRAe3whm5syZgiAIwm+//Sa0b99eOHbsmKDVaoXo6GjBy8tLKCgoEAoKCoSePXsKGzZsELRarXDgwAHB2dlZ+OKLLwRBEIRLly4JnTt3FpKTk4VHjx4Ju3btEnr16iUUFBSU2D9RZeEeBL3QLl68iDt37mDq1KlQKpVo2rQphg4div379wMA3njjDXh7e8Pc3BxjxoyBVqvF+fPnoVAooNVqce3aNRQWFqJJkyZ45ZVXiix///796NGjBzw8PGBubo5x48bh4cOHOHfuHM6fP4/CwkKMGjUK5ubm8Pb2Rtu2bQ3z7tixA2+++Sbat28PhUKBwMBAmJubIzk5ucz9E0nBk9T0Qrtx4wYyMzPh6upqeE2n08HV1RUODg5Gl4d+crXSJ+3nzp2LqKgopKamolu3bggNDS1yNdPMzEyjm/7I5XLDJb8VCgXs7Owgk8kM059uq9FosGfPHmzZssXwWmFhITIzM9GpU6cy9U8kBfcg6IVmb2+PJk2a4PTp04Z/586dw1dffQUARvc30Ov1yMjIMFw5VK1WY9u2bTh8+DBkMhk+++yzIsu3tbU1uqeDIAiGu8rZ2NggIyMDwlPXy3y6rb29PSZOnGhU2/nz5w23Ky1L/0RSMCDohdauXTvUrVsX0dHRePjwIXQ6Hf7v//4PFy5cAABcunQJP/zwAx49eoRNmzZBqVSiffv2+O2333D8+HFotVoolUrUqlVL9MY6Pj4+OHLkCI4fP47CwkKsX78eSqUSKpUKLi4uMDMzw+bNm1FYWIgffvgBFy9eNMw7ZMgQbN++HefPn4cgCMjPz8dPP/2E3NzcMvdPJAUPMdELTaFQYM2aNYiIiECfPn2g1WrRrFkzTJ8+HQDQp08f7N+/H7Nnz8arr76KqKgomJubQ6vV4vPPP8e1a9dgbm4OlUoleuvL5s2b49NPP8WCBQuQkZEBJycnrFmzxnAjnqioKHzwwQdYtmwZevTogb59+xrmbdu2LRYsWIDw8HD88ccfqF27Njp06ABXV9cy908kBe8HQVSMqKgo/PHHHzx0Qy8s7pMSEZEoBgQREYniISYiIhLFPQgiIhLFgCAiIlEMCCIiEsWAICIiUQwIIiISxYAgIiJR/w9MhqhgxjIp/gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cfg = QlearningConfig()\n",
    "plot_cfg = PlotConfig()\n",
    "# 训练\n",
    "env, agent = env_agent_config(cfg, seed=1)\n",
    "rewards, ma_rewards = train(cfg, env, agent)\n",
    "make_dir(plot_cfg.result_path, plot_cfg.model_path)  # 创建保存结果和模型路径的文件夹\n",
    "agent.save(path=plot_cfg.model_path)  # 保存模型\n",
    "save_results(rewards, ma_rewards, tag='train',\n",
    "            path=plot_cfg.result_path)  # 保存结果\n",
    "plot_rewards(rewards, ma_rewards, plot_cfg, tag=\"train\")  # 画出结果\n",
    "# 测试\n",
    "env, agent = env_agent_config(cfg, seed=10)\n",
    "agent.load(path=plot_cfg.model_path)  # 导入模型\n",
    "rewards, ma_rewards = test(cfg, env, agent)\n",
    "save_results(rewards, ma_rewards, tag='test', path=plot_cfg.result_path)  # 保存结果\n",
    "plot_rewards(rewards, ma_rewards, plot_cfg, tag=\"test\")  # 画出结果"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "fbea1422c2cf61ed9c0cfc03f38f71cc9083cc288606edc4170b5309b352ce27"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 64-bit ('py37': conda)",
   "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": "3.7.10"
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}