notebooks/第6章_Sarsa.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 定义算法"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sarsa 是一种同策略（on-policy）算法，它优化的是它实际执行的策略，它直接用下一步会执行的动作去优化 Q-table。\n",
    "其更新公式为：\n",
    "$$\n",
    "Q(s_t, a_t) \\leftarrow Q(s_t, a_t) + \\alpha  [r_{t+1} + \\gamma   Q(s_{t+1}, a_{t+1}) - Q(s, a)]\n",
    "$$\n",
    "- 同策略在学习的过程中，只存在一种策略，它用一种策略去做动作的选取，也用一种策略去做优化。Sarsa 算法必须执行两次动作得到$(s,a,r,s^\\prime,a^\\prime)$才可以更新一次；且$a^\\prime$是在特定策略$\\pi$的指导下执行的动作，因此估计出来的$Q(s_t,a_t)$是在策略$\\pi$下的$Q$值，样本生成用的$\\pi$和估计的$\\pi$是同一个，因此是同策略。\n",
    "- 该算法由于每次更新值函数时需要知道当前的状态（state）、当前的动作（action）、奖励（reward）、下一步的状态（state）、下一步的动作（action），即$(s,a,r,s^\\prime,a^\\prime)$这几个值 ，因此得名 Sarsa 算法。\n",
    "- Sarsa 和 Q-learning 的更新公式是一样的，区别只在目标计算的部分, Sarsa 是$r_{t+1}+\\gamma Q(s_{t+1},a_{t+1}) $， Q-learning 是$r_{t+1}+\\gamma \\max\\limits_a Q(s_{t+1},a) $。\n",
    "- 相比于异策略的 Q-learning ， Sarsa 作为同策略算法需要兼顾探索和利用，训练时相对较为保守，会选择一条相对安全的迭代路线。\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 定义模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "from collections import defaultdict\n",
    "\n",
    "class Sarsa(object):\n",
    "    def __init__(self,cfg):\n",
    "        '''智能体类\n",
    "        Args:\n",
    "            cfg (class): 超参数类\n",
    "        '''\n",
    "        self.n_actions = cfg.n_actions \n",
    "        self.lr = cfg.lr \n",
    "        self.gamma = cfg.gamma    \n",
    "        self.epsilon = cfg.epsilon_start\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(self.n_actions)) # 使用嵌套字典来表示 Q(s,a)，并将指定所有的 Q_table 创建时， Q(s,a) 初始设置为 0\n",
    "        self.sample_count = 0  \n",
    "    def sample_action(self, state):\n",
    "        ''' 另一种实现 e-greedy 策略的方式\n",
    "        Args:\n",
    "            state (array): 状态\n",
    "        Returns:\n",
    "            action (int): 动作\n",
    "        '''\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) # 选择随机动作的概率 epsilon 是对数衰减的\n",
    "        if np.random.uniform(0, 1) > self.epsilon:\n",
    "            action = np.argmax(self.Q_table[str(state)]) # 选取与最大 q 值相对应的动作\n",
    "        else:\n",
    "            action = np.random.choice(self.n_actions) # 随机选取动作\n",
    "        return action\n",
    "    def predict_action(self,state):\n",
    "        ''' 预测动作\n",
    "        Args:\n",
    "            state (array): 状态\n",
    "        Returns:\n",
    "            action (int): 动作\n",
    "        '''\n",
    "        action = np.argmax(self.Q_table[str(state)])\n",
    "        return action\n",
    "    def update(self, state, action, reward, next_state, next_action, done):\n",
    "        ''' 更新模型\n",
    "        Args:\n",
    "            state (array): 当前状态 \n",
    "            action (int): 当前动作 \n",
    "            reward (float): 当前奖励信号 \n",
    "            next_state (array): 下一个状态 \n",
    "            done (bool): 表示是否达到终止状态 \n",
    "        '''\n",
    "        Q_predict = self.Q_table[str(state)][action]\n",
    "        if done:\n",
    "            Q_target = reward  # 终止状态 \n",
    "        else:\n",
    "            Q_target = reward + self.gamma * self.Q_table[str(next_state)][next_action] # 与 Q-learning 的唯一区别\n",
    "        self.Q_table[str(state)][action] += self.lr * (Q_target - Q_predict) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 定义模型训练与测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(cfg, env, agent):\n",
    "    ''' 训练\n",
    "    '''\n",
    "    print(\"开始训练！\")\n",
    "    rewards = []  # 记录所有回合的奖励\n",
    "    steps = []\n",
    "    for i_ep in range(cfg.train_eps):\n",
    "        ep_reward = 0  # 一轮的累计奖励 \n",
    "        ep_step = 0\n",
    "        state = env.reset(seed = cfg.seed)  # 重置环境并获取初始状态\n",
    "        for _ in range(cfg.max_steps):\n",
    "            ep_step += 1\n",
    "            action = agent.sample_action(state)  # 采样动作 \n",
    "            next_state, reward, terminated, truncated , info = env.step(action)  # 更新环境并返回新状态、奖励、终止状态、截断标志和其他信息\n",
    "            next_action =  agent.sample_action(next_state)\n",
    "            agent.update(state, action, reward, next_state, next_action, terminated)  # 更新 agent\n",
    "            state = next_state  # 更新状态 \n",
    "            action = next_action # 更新动作\n",
    "            ep_reward += reward  # 增加奖励 \n",
    "            if terminated:\n",
    "                break\n",
    "        steps.append(ep_step)\n",
    "        rewards.append(ep_reward)\n",
    "        if (i_ep + 1) % 10 == 0:\n",
    "            print(f\"回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward:.2f}\")\n",
    "    print(\"完成训练！\")\n",
    "    return {'rewards':rewards}\n",
    "\n",
    "def test(cfg, env, agent):\n",
    "    '''测试\n",
    "    '''\n",
    "    print(\"开始测试！\")\n",
    "    rewards = []  # 记录所有回合的奖励\n",
    "    steps = []\n",
    "    print(cfg.test_eps)\n",
    "    for i_ep in range(cfg.test_eps):\n",
    "        ep_reward = 0  # 一轮的累计奖励 \n",
    "        ep_step = 0\n",
    "        state = env.reset(seed = cfg.seed)  # 重置环境并获取初始状态 \n",
    "        for _ in range(cfg.test_eps):\n",
    "            if cfg.render:\n",
    "                env.render()\n",
    "            ep_step += 1\n",
    "            action = agent.predict_action(state)  # 预测动作 \n",
    "            next_state, reward, terminated, truncated , info = env.step(action)   # 更新环境并返回新状态、奖励、终止状态、截断标志和其他信息\n",
    "            next_action =  agent.sample_action(next_state)\n",
    "            agent.update(state, action, reward, next_state, next_action, terminated)  # 更新 agent\n",
    "            state = next_state  # 更新状态 \n",
    "            action = next_action # 更新动作\n",
    "            ep_reward += reward  # 增加奖励\n",
    "            if terminated:\n",
    "                break\n",
    "        steps.append(ep_step)\n",
    "        rewards.append(ep_reward)\n",
    "        print(f\"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.2f}\")\n",
    "    print(\"完成测试\")\n",
    "    env.close()\n",
    "    return {'rewards':rewards}\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 定义环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gymnasium as gym\n",
    "import os\n",
    "import random\n",
    "import torch\n",
    "\n",
    "def all_seed(env,seed = 1):\n",
    "    ''' 万能的seed函数\n",
    "    '''\n",
    "    env.reset(seed=seed) # 环境配置\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    torch.manual_seed(seed) # CPU配置\n",
    "    torch.cuda.manual_seed(seed) # GPU配置\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed) # python scripts配置\n",
    "    # cudnn配置\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    torch.backends.cudnn.enabled = False\n",
    "def env_agent_config(cfg):\n",
    "    env = gym.make(cfg.env_name) # 创建环境\n",
    "    all_seed(env,seed=cfg.seed)\n",
    "    print(env.observation_space.shape)\n",
    "    n_states = env.observation_space.n\n",
    "    n_actions = env.action_space.n\n",
    "    print(f\"状态空间维度：{n_states}，动作空间维度：{n_actions}\")\n",
    "    # 更新n_states和n_actions到cfg参数中\n",
    "    setattr(cfg, 'n_states', n_states)\n",
    "    setattr(cfg, 'n_actions', n_actions) \n",
    "    setattr(cfg, 'action_space', env.action_space) \n",
    "    agent = Sarsa(cfg)\n",
    "    return env,agent"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 设置参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "class Config:\n",
    "    def __init__(self) -> None:\n",
    "        ## 通用参数\n",
    "        self.env_name = \"CliffWalking-v0\" # 环境名称\n",
    "        self.render = False # 是否渲染环境\n",
    "        self.algo_name = \"Sarsa\" # 算法的名称\n",
    "        self.seed = 1 # 随机种子\n",
    "        self.device = \"cuda\" # 用的device \n",
    "        self.train_eps = 300 # training的episodes数\n",
    "        self.test_eps = 10 # testing的episodes数\n",
    "        self.max_steps = 1000 # 每episode的最大steps数\n",
    "\n",
    "        ## Sarsa参数\n",
    "        self.epsilon_start = 0.95 # epsilon 初始值\n",
    "        self.epsilon_end = 0.01 # epsilon 终止值\n",
    "        self.epsilon_decay = 300 # epsilon 衰减率\n",
    "        self.gamma = 0.90 # 奖励折扣因子\n",
    "        self.lr = 0.1 # 学习率\n",
    "\n",
    "def smooth(data, weight=0.9):  \n",
    "    '''用于平滑曲线，类似于Tensorboard中的smooth曲线\n",
    "    '''\n",
    "    last = data[0] \n",
    "    smoothed = []\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()\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取参数\n",
    "cfg = Config() \n",
    "env, agent = env_agent_config(cfg)\n",
    "# 训练\n",
    "res_dic = train(cfg, env, agent)\n",
    "plot_rewards(res_dic['rewards'], title=f\"training curve on {cfg.device} of {cfg.algo_name} for {cfg.env_name}\") # 画出结果\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"
   ]
  }
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