{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 蒙特卡洛算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、定义算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "class FisrtVisitMC:\n",
    "    ''' On-Policy First-Visit MC Control\n",
    "    '''\n",
    "    def __init__(self,cfg):\n",
    "        self.n_actions = cfg.n_actions\n",
    "        self.epsilon = cfg.epsilon\n",
    "        self.gamma = cfg.gamma \n",
    "        self.Q_table = defaultdict(lambda: np.zeros(cfg.n_actions))\n",
    "        self.returns_sum = defaultdict(float) # 保存return之和\n",
    "        self.returns_count = defaultdict(float)\n",
    "        \n",
    "    def sample_action(self,state):\n",
    "        state = str(state)\n",
    "        if state in self.Q_table.keys():\n",
    "            best_action = np.argmax(self.Q_table[state])\n",
    "            action_probs = np.ones(self.n_actions, dtype=float) * self.epsilon / self.n_actions\n",
    "            action_probs[best_action] += (1.0 - self.epsilon)\n",
    "            action = np.random.choice(np.arange(len(action_probs)), p=action_probs)\n",
    "        else:\n",
    "            action = np.random.randint(0,self.n_actions)\n",
    "        return action\n",
    "    def predict_action(self,state):\n",
    "        state = str(state)\n",
    "        if state in self.Q_table.keys():\n",
    "            best_action = np.argmax(self.Q_table[state])\n",
    "            action_probs = np.ones(self.n_actions, dtype=float) * self.epsilon / self.n_actions\n",
    "            action_probs[best_action] += (1.0 - self.epsilon)\n",
    "            action = np.argmax(self.Q_table[state])\n",
    "        else:\n",
    "            action = np.random.randint(0,self.n_actions)\n",
    "        return action\n",
    "    def update(self,one_ep_transition):\n",
    "        # Find all (state, action) pairs we've visited in this one_ep_transition\n",
    "        # We convert each state to a tuple so that we can use it as a dict key\n",
    "        sa_in_episode = set([(str(x[0]), x[1]) for x in one_ep_transition])\n",
    "        for state, action in sa_in_episode:\n",
    "            sa_pair = (state, action)\n",
    "            # Find the first occurence of the (state, action) pair in the one_ep_transition\n",
    "\n",
    "            first_occurence_idx = next(i for i,x in enumerate(one_ep_transition)\n",
    "                                       if str(x[0]) == state and x[1] == action)\n",
    "            # Sum up all rewards since the first occurance\n",
    "            G = sum([x[2]*(self.gamma**i) for i,x in enumerate(one_ep_transition[first_occurence_idx:])])\n",
    "            # Calculate average return for this state over all sampled episodes\n",
    "            self.returns_sum[sa_pair] += G\n",
    "            self.returns_count[sa_pair] += 1.0\n",
    "            self.Q_table[state][action] = self.returns_sum[sa_pair] / self.returns_count[sa_pair]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、定义训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "    for i_ep in range(cfg.train_eps):\n",
    "        ep_reward = 0  # 记录每个回合的奖励\n",
    "        one_ep_transition = []\n",
    "        state = env.reset(seed=cfg.seed) # 重置环境,即开始新的回合\n",
    "        for _ in range(cfg.max_steps):\n",
    "            action = agent.sample_action(state)  # 根据算法采样一个动作\n",
    "            next_state, reward, terminated, info = env.step(action)   # 与环境进行一次动作交互\n",
    "            one_ep_transition.append((state, action, reward))  # 保存transitions\n",
    "            agent.update(one_ep_transition)  # 更新智能体\n",
    "            state = next_state  # 更新状态\n",
    "            ep_reward += reward  \n",
    "            if terminated:\n",
    "                break\n",
    "        rewards.append(ep_reward)\n",
    "        print(f\"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.1f}\")\n",
    "    print('完成训练！')\n",
    "    return {\"rewards\":rewards}\n",
    "def test(cfg,env,agent):\n",
    "    print('开始测试！')\n",
    "    print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')\n",
    "    rewards = []  # 记录所有回合的奖励\n",
    "    for i_ep in range(cfg.test_eps):\n",
    "        ep_reward = 0  # 记录每个episode的reward\n",
    "        state = env.reset(seed=cfg.seed)  # 重置环境, 重新开一局（即开始新的一个回合）\n",
    "        for _ in range(cfg.max_steps):\n",
    "            action = agent.predict_action(state)  # 根据算法选择一个动作\n",
    "            next_state, reward, terminated, info = env.step(action)  # 与环境进行一个交互\n",
    "            state = next_state  # 更新状态\n",
    "            ep_reward += reward\n",
    "            if terminated:\n",
    "                break\n",
    "        rewards.append(ep_reward)\n",
    "        print(f\"回合数：{i_ep+1}/{cfg.test_eps}, 奖励：{ep_reward:.1f}\")\n",
    "    print('完成测试！')\n",
    "    return {\"rewards\":rewards}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、定义环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys,os\n",
    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), \"../..\")))\n",
    "import torch\n",
    "import numpy as np\n",
    "import random\n",
    "from envs.racetrack import RacetrackEnv\n",
    "\n",
    "def all_seed(env,seed = 1):\n",
    "    ''' omnipotent seed for RL, attention the position of seed function, you'd better put it just following the env create function\n",
    "    '''\n",
    "    if seed == 0:\n",
    "        return\n",
    "    # print(f\"seed = {seed}\")\n",
    "    env.seed(seed) # env config\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    torch.manual_seed(seed) # config for CPU\n",
    "    torch.cuda.manual_seed(seed) # config for GPU\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed) # config for python scripts\n",
    "    # config for cudnn\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    torch.backends.cudnn.enabled = False\n",
    "    \n",
    "def env_agent_config(cfg):\n",
    "    '''创建环境和智能体\n",
    "    '''    \n",
    "    env = RacetrackEnv()  # 创建环境\n",
    "    all_seed(env,seed=cfg.seed) \n",
    "    n_states = env.observation_space.shape[0]  # 状态空间维度\n",
    "    n_actions = env.action_space.n # 动作空间维度\n",
    "    setattr(cfg, 'n_states', n_states) # 将状态维度添加到配置参数中\n",
    "    setattr(cfg, 'n_actions', n_actions) # 将动作维度添加到配置参数中\n",
    "    agent = FisrtVisitMC(cfg)\n",
    "    return env,agent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4、设置参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "class Config:\n",
    "    '''配置参数\n",
    "    '''\n",
    "    def __init__(self):\n",
    "        self.env_name = 'Racetrack-v0' # 环境名称\n",
    "        self.algo_name = \"FirstVisitMC\" # 算法名称\n",
    "        self.train_eps = 400 # 训练回合数\n",
    "        self.test_eps = 20 # 测试回合数\n",
    "        self.max_steps = 200 # 每个回合最大步数\n",
    "        self.epsilon = 0.1 # 贪婪度\n",
    "        self.gamma = 0.9 # 折扣因子\n",
    "        self.lr = 0.5 # 学习率\n",
    "        self.seed = 1 # 随机种子\n",
    "        # if torch.cuda.is_available(): # 是否使用GPUs\n",
    "        #     self.device = torch.device('cuda')\n",
    "        # else:\n",
    "        #     self.device = torch.device('cpu')\n",
    "        self.device = torch.device('cpu')\n",
    "def smooth(data, weight=0.9):  \n",
    "    '''用于平滑曲线\n",
    "    '''\n",
    "    last = data[0]  # First value in the plot (first timestep)\n",
    "    smoothed = list()\n",
    "    for point in data:\n",
    "        smoothed_val = last * weight + (1 - weight) * point  # 计算平滑值\n",
    "        smoothed.append(smoothed_val)                    \n",
    "        last = smoothed_val                                \n",
    "    return smoothed\n",
    "\n",
    "def plot_rewards(rewards,title=\"learning curve\"):\n",
    "    sns.set()\n",
    "    plt.figure()  # 创建一个图形实例，方便同时多画几个图\n",
    "    plt.title(f\"{title}\")\n",
    "    plt.xlim(0, len(rewards), 10)  # 设置x轴的范围\n",
    "    plt.xlabel('epsiodes')\n",
    "    plt.plot(rewards, label='rewards')\n",
    "    plt.plot(smooth(rewards), label='smoothed')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5、开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\24438\\anaconda3\\envs\\easyrl\\lib\\site-packages\\gym\\core.py:257: DeprecationWarning: \u001b[33mWARN: Function `env.seed(seed)` is marked as deprecated and will be removed in the future. Please use `env.reset(seed=seed)` instead.\u001b[0m\n",
      "  \"Function `env.seed(seed)` is marked as deprecated and will be removed in the future. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练！\n",
      "环境:Racetrack-v0, 算法:FirstVisitMC, 设备:cpu\n",
      "回合：1/400，奖励：-680.0\n",
      "回合：2/400，奖励：-510.0\n",
      "回合：3/400，奖励：-360.0\n",
      "回合：4/400，奖励：-440.0\n",
      "回合：5/400，奖励：-410.0\n",
      "回合：6/400，奖励：-380.0\n",
      "回合：7/400，奖励：-400.0\n",
      "回合：8/400，奖励：-360.0\n",
      "回合：9/400，奖励：-360.0\n",
      "回合：10/400，奖励：-350.0\n",
      "回合：11/400，奖励：-320.0\n",
      "回合：12/400，奖励：-380.0\n",
      "回合：13/400，奖励：-360.0\n",
      "回合：14/400，奖励：-350.0\n",
      "回合：15/400，奖励：-310.0\n",
      "回合：16/400，奖励：-310.0\n",
      "回合：17/400，奖励：-340.0\n",
      "回合：18/400，奖励：-84.0\n",
      "回合：19/400，奖励：-310.0\n",
      "回合：20/400，奖励：-104.0\n",
      "回合：21/400，奖励：-370.0\n",
      "回合：22/400，奖励：-330.0\n",
      "回合：23/400，奖励：-350.0\n",
      "回合：24/400，奖励：-350.0\n",
      "回合：25/400，奖励：-380.0\n",
      "回合：26/400，奖励：-410.0\n",
      "回合：27/400，奖励：-310.0\n",
      "回合：28/400，奖励：-260.0\n",
      "回合：29/400，奖励：-21.0\n",
      "回合：30/400，奖励：-310.0\n",
      "回合：31/400，奖励：-350.0\n",
      "回合：32/400，奖励：-400.0\n",
      "回合：33/400，奖励：-290.0\n",
      "回合：34/400，奖励：-340.0\n",
      "回合：35/400，奖励：-320.0\n",
      "回合：36/400，奖励：-360.0\n",
      "回合：37/400，奖励：-262.0\n",
      "回合：38/400，奖励：-370.0\n",
      "回合：39/400，奖励：-69.0\n",
      "回合：40/400，奖励：-170.0\n",
      "回合：41/400，奖励：-310.0\n",
      "回合：42/400，奖励：-300.0\n",
      "回合：43/400，奖励：-280.0\n",
      "回合：44/400，奖励：-310.0\n",
      "回合：45/400，奖励：-320.0\n",
      "回合：46/400，奖励：-320.0\n",
      "回合：47/400，奖励：-330.0\n",
      "回合：48/400，奖励：-330.0\n",
      "回合：49/400，奖励：-360.0\n",
      "回合：50/400，奖励：-350.0\n",
      "回合：51/400，奖励：-320.0\n",
      "回合：52/400，奖励：-400.0\n",
      "回合：53/400，奖励：-330.0\n",
      "回合：54/400，奖励：-250.0\n",
      "回合：55/400，奖励：-340.0\n",
      "回合：56/400，奖励：-310.0\n",
      "回合：57/400，奖励：-300.0\n",
      "回合：58/400，奖励：-320.0\n",
      "回合：59/400，奖励：-280.0\n",
      "回合：60/400，奖励：-290.0\n",
      "回合：61/400，奖励：-360.0\n",
      "回合：62/400，奖励：-270.0\n",
      "回合：63/400，奖励：-310.0\n",
      "回合：64/400，奖励：-370.0\n",
      "回合：65/400，奖励：-330.0\n",
      "回合：66/400，奖励：-290.0\n",
      "回合：67/400，奖励：-330.0\n",
      "回合：68/400，奖励：-400.0\n",
      "回合：69/400，奖励：-360.0\n",
      "回合：70/400，奖励：-310.0\n",
      "回合：71/400，奖励：-250.0\n",
      "回合：72/400，奖励：-280.0\n",
      "回合：73/400，奖励：-98.0\n",
      "回合：74/400，奖励：-270.0\n",
      "回合：75/400，奖励：-360.0\n",
      "回合：76/400，奖励：-270.0\n",
      "回合：77/400，奖励：-310.0\n",
      "回合：78/400，奖励：-290.0\n",
      "回合：79/400，奖励：-300.0\n",
      "回合：80/400，奖励：-320.0\n",
      "回合：81/400，奖励：-280.0\n",
      "回合：82/400，奖励：-300.0\n",
      "回合：83/400，奖励：-260.0\n",
      "回合：84/400，奖励：-290.0\n",
      "回合：85/400，奖励：-249.0\n",
      "回合：86/400，奖励：-320.0\n",
      "回合：87/400，奖励：-300.0\n",
      "回合：88/400，奖励：-300.0\n",
      "回合：89/400，奖励：-300.0\n",
      "回合：90/400，奖励：-300.0\n",
      "回合：91/400，奖励：-70.0\n",
      "回合：92/400，奖励：-310.0\n",
      "回合：93/400，奖励：-114.0\n",
      "回合：94/400，奖励：-280.0\n",
      "回合：95/400，奖励：-180.0\n",
      "回合：96/400，奖励：-310.0\n",
      "回合：97/400，奖励：-320.0\n",
      "回合：98/400，奖励：-340.0\n",
      "回合：99/400，奖励：-210.0\n",
      "回合：100/400，奖励：-4.0\n",
      "回合：101/400，奖励：-300.0\n",
      "回合：102/400，奖励：-290.0\n",
      "回合：103/400，奖励：-270.0\n",
      "回合：104/400，奖励：-370.0\n",
      "回合：105/400，奖励：-241.0\n",
      "回合：106/400，奖励：-300.0\n",
      "回合：107/400，奖励：-280.0\n",
      "回合：108/400，奖励：-320.0\n",
      "回合：109/400，奖励：-330.0\n",
      "回合：110/400，奖励：-290.0\n",
      "回合：111/400，奖励：-300.0\n",
      "回合：112/400，奖励：-270.0\n",
      "回合：113/400，奖励：-260.0\n",
      "回合：114/400，奖励：-320.0\n",
      "回合：115/400，奖励：-260.0\n",
      "回合：116/400，奖励：-310.0\n",
      "回合：117/400，奖励：-250.0\n",
      "回合：118/400，奖励：-330.0\n",
      "回合：119/400，奖励：-108.0\n",
      "回合：120/400，奖励：-270.0\n",
      "回合：121/400，奖励：-340.0\n",
      "回合：122/400，奖励：-290.0\n",
      "回合：123/400，奖励：-310.0\n",
      "回合：124/400，奖励：-4.0\n",
      "回合：125/400，奖励：-290.0\n",
      "回合：126/400，奖励：-280.0\n",
      "回合：127/400，奖励：-250.0\n",
      "回合：128/400，奖励：-36.0\n",
      "回合：129/400，奖励：-290.0\n",
      "回合：130/400，奖励：-300.0\n",
      "回合：131/400，奖励：-290.0\n",
      "回合：132/400，奖励：-310.0\n",
      "回合：133/400，奖励：-320.0\n",
      "回合：134/400，奖励：-290.0\n",
      "回合：135/400，奖励：-260.0\n",
      "回合：136/400，奖励：-270.0\n",
      "回合：137/400，奖励：-290.0\n",
      "回合：138/400，奖励：-230.0\n",
      "回合：139/400，奖励：-95.0\n",
      "回合：140/400，奖励：-260.0\n",
      "回合：141/400，奖励：-105.0\n",
      "回合：142/400，奖励：-237.0\n",
      "回合：143/400，奖励：-270.0\n",
      "回合：144/400，奖励：-280.0\n",
      "回合：145/400，奖励：-166.0\n",
      "回合：146/400，奖励：-259.0\n",
      "回合：147/400，奖励：-16.0\n",
      "回合：148/400，奖励：-300.0\n",
      "回合：149/400，奖励：-260.0\n",
      "回合：150/400，奖励：-227.0\n",
      "回合：151/400，奖励：-260.0\n",
      "回合：152/400，奖励：-240.0\n",
      "回合：153/400，奖励：-300.0\n",
      "回合：154/400，奖励：-240.0\n",
      "回合：155/400，奖励：-320.0\n",
      "回合：156/400，奖励：-65.0\n",
      "回合：157/400，奖励：-310.0\n",
      "回合：158/400，奖励：-340.0\n",
      "回合：159/400，奖励：-300.0\n",
      "回合：160/400，奖励：-52.0\n",
      "回合：161/400，奖励：-232.0\n",
      "回合：162/400，奖励：-179.0\n",
      "回合：163/400，奖励：-260.0\n",
      "回合：164/400，奖励：-98.0\n",
      "回合：165/400，奖励：-310.0\n",
      "回合：166/400，奖励：-246.0\n",
      "回合：167/400，奖励：-1.0\n",
      "回合：168/400，奖励：-340.0\n",
      "回合：169/400，奖励：-182.0\n",
      "回合：170/400，奖励：-240.0\n",
      "回合：171/400，奖励：-290.0\n",
      "回合：172/400，奖励：-133.0\n",
      "回合：173/400，奖励：-260.0\n",
      "回合：174/400，奖励：-58.0\n",
      "回合：175/400，奖励：-100.0\n",
      "回合：176/400，奖励：-287.0\n",
      "回合：177/400，奖励：-280.0\n",
      "回合：178/400，奖励：-166.0\n",
      "回合：179/400，奖励：-310.0\n",
      "回合：180/400，奖励：-2.0\n",
      "回合：181/400，奖励：-250.0\n",
      "回合：182/400，奖励：-310.0\n",
      "回合：183/400，奖励：-106.0\n",
      "回合：184/400，奖励：-300.0\n",
      "回合：185/400，奖励：1.0\n",
      "回合：186/400，奖励：-54.0\n",
      "回合：187/400，奖励：-270.0\n",
      "回合：188/400，奖励：-260.0\n",
      "回合：189/400，奖励：-250.0\n",
      "回合：190/400，奖励：-184.0\n",
      "回合：191/400，奖励：-290.0\n",
      "回合：192/400，奖励：-310.0\n",
      "回合：193/400，奖励：1.0\n",
      "回合：194/400，奖励：-96.0\n",
      "回合：195/400，奖励：-180.0\n",
      "回合：196/400，奖励：-280.0\n",
      "回合：197/400，奖励：-310.0\n",
      "回合：198/400，奖励：-310.0\n",
      "回合：199/400，奖励：-240.0\n",
      "回合：200/400，奖励：-230.0\n",
      "回合：201/400，奖励：-108.0\n",
      "回合：202/400，奖励：-72.0\n",
      "回合：203/400，奖励：-260.0\n",
      "回合：204/400，奖励：-270.0\n",
      "回合：205/400，奖励：-12.0\n",
      "回合：206/400，奖励：-9.0\n",
      "回合：207/400，奖励：-103.0\n",
      "回合：208/400，奖励：0.0\n",
      "回合：209/400，奖励：-67.0\n",
      "回合：210/400，奖励：-167.0\n",
      "回合：211/400，奖励：-290.0\n",
      "回合：212/400，奖励：-280.0\n",
      "回合：213/400，奖励：-192.0\n",
      "回合：214/400，奖励：-184.0\n",
      "回合：215/400，奖励：-30.0\n",
      "回合：216/400，奖励：-300.0\n",
      "回合：217/400，奖励：-58.0\n",
      "回合：218/400，奖励：-290.0\n",
      "回合：219/400，奖励：-185.0\n",
      "回合：220/400，奖励：-270.0\n",
      "回合：221/400，奖励：-231.0\n",
      "回合：222/400，奖励：-178.0\n",
      "回合：223/400，奖励：-48.0\n",
      "回合：224/400，奖励：-260.0\n",
      "回合：225/400，奖励：-240.0\n",
      "回合：226/400，奖励：-160.0\n",
      "回合：227/400，奖励：-250.0\n",
      "回合：228/400，奖励：1.0\n",
      "回合：229/400，奖励：-75.0\n",
      "回合：230/400，奖励：-249.0\n",
      "回合：231/400，奖励：-10.0\n",
      "回合：232/400，奖励：-60.0\n",
      "回合：233/400，奖励：-290.0\n",
      "回合：234/400，奖励：1.0\n",
      "回合：235/400，奖励：-250.0\n",
      "回合：236/400，奖励：-320.0\n",
      "回合：237/400，奖励：-97.0\n",
      "回合：238/400，奖励：-225.0\n",
      "回合：239/400，奖励：-320.0\n",
      "回合：240/400，奖励：-250.0\n",
      "回合：241/400，奖励：-127.0\n",
      "回合：242/400，奖励：-270.0\n",
      "回合：243/400，奖励：-230.0\n",
      "回合：244/400，奖励：-50.0\n",
      "回合：245/400，奖励：-171.0\n",
      "回合：246/400，奖励：-270.0\n",
      "回合：247/400，奖励：-19.0\n",
      "回合：248/400，奖励：-119.0\n",
      "回合：249/400，奖励：-18.0\n",
      "回合：250/400，奖励：-41.0\n",
      "回合：251/400，奖励：-290.0\n",
      "回合：252/400，奖励：-88.0\n",
      "回合：253/400，奖励：-270.0\n",
      "回合：254/400，奖励：-280.0\n",
      "回合：255/400，奖励：-300.0\n",
      "回合：256/400，奖励：-250.0\n",
      "回合：257/400，奖励：-91.0\n",
      "回合：258/400，奖励：-270.0\n",
      "回合：259/400，奖励：-109.0\n",
      "回合：260/400，奖励：-330.0\n",
      "回合：261/400，奖励：-320.0\n",
      "回合：262/400，奖励：-280.0\n",
      "回合：263/400，奖励：-240.0\n",
      "回合：264/400，奖励：-250.0\n",
      "回合：265/400，奖励：-240.0\n",
      "回合：266/400，奖励：1.0\n",
      "回合：267/400，奖励：-310.0\n",
      "回合：268/400，奖励：-290.0\n",
      "回合：269/400，奖励：-170.0\n",
      "回合：270/400，奖励：-104.0\n",
      "回合：271/400，奖励：-166.0\n",
      "回合：272/400，奖励：-290.0\n",
      "回合：273/400，奖励：-11.0\n",
      "回合：274/400，奖励：-290.0\n",
      "回合：275/400，奖励：-107.0\n",
      "回合：276/400，奖励：-156.0\n",
      "回合：277/400，奖励：-280.0\n",
      "回合：278/400，奖励：-242.0\n",
      "回合：279/400，奖励：-260.0\n",
      "回合：280/400，奖励：-31.0\n",
      "回合：281/400，奖励：-165.0\n",
      "回合：282/400，奖励：1.0\n",
      "回合：283/400，奖励：-139.0\n",
      "回合：284/400，奖励：-129.0\n",
      "回合：285/400，奖励：-87.0\n",
      "回合：286/400，奖励：-109.0\n",
      "回合：287/400，奖励：-89.0\n",
      "回合：288/400，奖励：-240.0\n",
      "回合：289/400，奖励：-95.0\n",
      "回合：290/400，奖励：-152.0\n",
      "回合：291/400，奖励：-43.0\n",
      "回合：292/400，奖励：-42.0\n",
      "回合：293/400，奖励：-270.0\n",
      "回合：294/400，奖励：-84.0\n",
      "回合：295/400，奖励：-300.0\n",
      "回合：296/400，奖励：-260.0\n",
      "回合：297/400，奖励：-260.0\n",
      "回合：298/400，奖励：-83.0\n",
      "回合：299/400，奖励：-56.0\n",
      "回合：300/400，奖励：-77.0\n",
      "回合：301/400，奖励：-176.0\n",
      "回合：302/400，奖励：-103.0\n",
      "回合：303/400，奖励：-215.0\n",
      "回合：304/400，奖励：-182.0\n",
      "回合：305/400，奖励：2.0\n",
      "回合：306/400，奖励：-182.0\n",
      "回合：307/400，奖励：-33.0\n",
      "回合：308/400，奖励：-36.0\n",
      "回合：309/400，奖励：-142.0\n",
      "回合：310/400，奖励：-26.0\n",
      "回合：311/400，奖励：-185.0\n",
      "回合：312/400，奖励：-250.0\n",
      "回合：313/400，奖励：1.0\n",
      "回合：314/400，奖励：-73.0\n",
      "回合：315/400，奖励：-152.0\n",
      "回合：316/400，奖励：-133.0\n",
      "回合：317/400，奖励：-270.0\n",
      "回合：318/400，奖励：-46.0\n",
      "回合：319/400，奖励：-270.0\n",
      "回合：320/400，奖励：2.0\n",
      "回合：321/400，奖励：-280.0\n",
      "回合：322/400，奖励：-330.0\n",
      "回合：323/400，奖励：-300.0\n",
      "回合：324/400，奖励：-29.0\n",
      "回合：325/400，奖励：-246.0\n",
      "回合：326/400，奖励：-300.0\n",
      "回合：327/400，奖励：-124.0\n",
      "回合：328/400，奖励：-81.0\n",
      "回合：329/400，奖励：-280.0\n",
      "回合：330/400，奖励：-127.0\n",
      "回合：331/400，奖励：-270.0\n",
      "回合：332/400，奖励：-310.0\n",
      "回合：333/400，奖励：-270.0\n",
      "回合：334/400，奖励：-270.0\n",
      "回合：335/400，奖励：-76.0\n",
      "回合：336/400，奖励：-260.0\n",
      "回合：337/400，奖励：-160.0\n",
      "回合：338/400，奖励：-135.0\n",
      "回合：339/400，奖励：-214.0\n",
      "回合：340/400，奖励：-176.0\n",
      "回合：341/400，奖励：-28.0\n",
      "回合：342/400，奖励：-280.0\n",
      "回合：343/400，奖励：-75.0\n",
      "回合：344/400，奖励：-65.0\n",
      "回合：345/400，奖励：-17.0\n",
      "回合：346/400，奖励：-162.0\n",
      "回合：347/400，奖励：-250.0\n",
      "回合：348/400，奖励：-134.0\n",
      "回合：349/400，奖励：-165.0\n",
      "回合：350/400，奖励：-128.0\n",
      "回合：351/400，奖励：-250.0\n",
      "回合：352/400，奖励：-186.0\n",
      "回合：353/400，奖励：-250.0\n",
      "回合：354/400，奖励：-9.0\n",
      "回合：355/400，奖励：-12.0\n",
      "回合：356/400，奖励：-127.0\n",
      "回合：357/400，奖励：-155.0\n",
      "回合：358/400，奖励：-15.0\n",
      "回合：359/400，奖励：-290.0\n",
      "回合：360/400，奖励：-260.0\n",
      "回合：361/400，奖励：-250.0\n",
      "回合：362/400，奖励：-260.0\n",
      "回合：363/400，奖励：-180.0\n",
      "回合：364/400，奖励：-19.0\n",
      "回合：365/400，奖励：-300.0\n",
      "回合：366/400，奖励：-157.0\n",
      "回合：367/400，奖励：-11.0\n",
      "回合：368/400，奖励：-58.0\n",
      "回合：369/400，奖励：-46.0\n",
      "回合：370/400，奖励：-212.0\n",
      "回合：371/400，奖励：-134.0\n",
      "回合：372/400，奖励：-220.0\n",
      "回合：373/400，奖励：-243.0\n",
      "回合：374/400，奖励：-28.0\n",
      "回合：375/400，奖励：-3.0\n",
      "回合：376/400，奖励：-240.0\n",
      "回合：377/400，奖励：-54.0\n",
      "回合：378/400，奖励：-230.0\n",
      "回合：379/400，奖励：-98.0\n",
      "回合：380/400，奖励：-83.0\n",
      "回合：381/400，奖励：-81.0\n",
      "回合：382/400，奖励：-290.0\n",
      "回合：383/400，奖励：-270.0\n",
      "回合：384/400，奖励：-53.0\n",
      "回合：385/400，奖励：-38.0\n",
      "回合：386/400，奖励：-97.0\n",
      "回合：387/400，奖励：-69.0\n",
      "回合：388/400，奖励：-270.0\n",
      "回合：389/400，奖励：-240.0\n",
      "回合：390/400，奖励：-56.0\n",
      "回合：391/400，奖励：-8.0\n",
      "回合：392/400，奖励：-19.0\n",
      "回合：393/400，奖励：-191.0\n",
      "回合：394/400，奖励：-230.0\n",
      "回合：395/400，奖励：-57.0\n",
      "回合：396/400，奖励：-142.0\n",
      "回合：397/400，奖励：-41.0\n",
      "回合：398/400，奖励：-247.0\n",
      "回合：399/400，奖励：-240.0\n",
      "回合：400/400，奖励：2.0\n",
      "完成训练！\n",
      "开始测试！\n",
      "环境：Racetrack-v0, 算法：FirstVisitMC, 设备：cpu\n",
      "回合数：1/20, 奖励：-200.0\n",
      "回合数：2/20, 奖励：-210.0\n",
      "回合数：3/20, 奖励：-200.0\n",
      "回合数：4/20, 奖励：-200.0\n",
      "回合数：5/20, 奖励：-200.0\n",
      "回合数：6/20, 奖励：-200.0\n",
      "回合数：7/20, 奖励：-200.0\n",
      "回合数：8/20, 奖励：-200.0\n",
      "回合数：9/20, 奖励：-200.0\n",
      "回合数：10/20, 奖励：2.0\n",
      "回合数：11/20, 奖励：-200.0\n",
      "回合数：12/20, 奖励：-200.0\n",
      "回合数：13/20, 奖励：-200.0\n",
      "回合数：14/20, 奖励：-200.0\n",
      "回合数：15/20, 奖励：-200.0\n",
      "回合数：16/20, 奖励：-200.0\n",
      "回合数：17/20, 奖励：-200.0\n",
      "回合数：18/20, 奖励：-200.0\n",
      "回合数：19/20, 奖励：-200.0\n",
      "回合数：20/20, 奖励：-200.0\n",
      "完成测试！\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 获取参数\n",
    "cfg = Config() \n",
    "# 训练\n",
    "env, agent = env_agent_config(cfg)\n",
    "res_dic = train(cfg, env, agent)\n",
    " \n",
    "plot_rewards(res_dic['rewards'], title=f\"training curve on {cfg.device} of {cfg.algo_name} for {cfg.env_name}\")  \n",
    "# 测试\n",
    "res_dic = test(cfg, env, agent)\n",
    "plot_rewards(res_dic['rewards'], title=f\"testing curve on {cfg.device} of {cfg.algo_name} for {cfg.env_name}\")  # 画出结果"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.12 ('easyrl')",
   "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": "3.7.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "f5a9629e9f3b9957bf68a43815f911e93447d47b3d065b6a8a04975e44c504d9"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}