写一个基于CPU的DQN算法

下面是一个基于CPU的DQN算法的简单实现：

import random
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from collections import namedtuple
from itertools import count

# 设置随机种子
random.seed(0)
torch.manual_seed(0)

# 定义超参数
BATCH_SIZE = 32
GAMMA = 0.99
EPS_START = 1.0
EPS_END = 0.01
EPS_DECAY = 500
TARGET_UPDATE = 10

# 定义模型
class DQN(nn.Module):
    def __init__(self, num_inputs, num_actions):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(num_inputs, 128)
        self.fc2 = nn.Linear(128, 128)
        self.fc3 = nn.Linear(128, num_actions)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义经验回放内存
Transition = namedtuple('Transition', ('state', 'action', 'next_state', 'reward'))

class ReplayMemory(object):
    def __init__(self, capacity):
        self.capacity = capacity
        self.memory = []
        self.position = 0

    def push(self, *args):
        if len(self.memory) < self.capacity:
            self.memory.append(None)
        self.memory[self.position] = Transition(*args)
        self.position = (self.position + 1) % self.capacity

    def sample(self, batch_size):
        return random.sample(self.memory, batch_size)

    def __len__(self):
        return len(self.memory)

# 定义DQN算法
class DQNAgent(object):
    def __init__(self, num_inputs, num_actions):
        self.num_inputs = num_inputs
        self.num_actions = num_actions

        # 初始化网络和优化器
        self.policy_net = DQN(num_inputs, num_actions)
        self.target_net = DQN(num_inputs, num_actions)
        self.target_net.load_state_dict(self.policy_net.state_dict())
        self.target_net.eval()

        self.optimizer = optim.Adam(self.policy_net.parameters())

        # 初始化经验回放内存
        self.memory = ReplayMemory(10000)

        # 初始化epsilon
        self.steps_done = 0

    def select_action(self, state, epsilon):
        sample = random.random()
        eps_threshold = epsilon
        self.steps_done += 1
        if sample > eps_threshold:
            with torch.no_grad():
                state = torch.FloatTensor(state).unsqueeze(0)
                q_values = self.policy_net(state)
                action = q_values.max(1)[1].item()
        else:
            action = random.randrange(self.num_actions)
        return action

    def optimize_model(self):
        if len(self.memory) < BATCH_SIZE:
            return
        transitions = self.memory.sample(BATCH_SIZE)
        batch = Transition(*zip(*transitions))

        # 计算当前状态的Q值
        state_batch = torch.FloatTensor(batch.state)
        action_batch = torch.LongTensor(batch.action)
        reward_batch = torch.FloatTensor(batch.reward)
        next_state_batch = torch.FloatTensor(batch.next_state)

        state_action_values = self.policy_net(state_batch).gather(1, action_batch.unsqueeze(1))

        # 计算目标Q值
        next_state_values = self.target_net(next_state_batch).max(1)[0].detach()
        expected_state_action_values = (next_state_values * GAMMA) + reward_batch

        # 计算损失函数
        loss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1))

        # 优化网络
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

    def update_target_model(self):
        self.target_net.load_state_dict(self.policy_net.state_dict())

# 创建DQNAgent
agent = DQNAgent(num_inputs=4, num_actions=2)

# 训练模型
num_episodes = 1000
for i_episode in range(num_episodes):
    # 初始化环境和状态
    state = env.reset()
    total_reward = 0
    for t in count():
        # 选择动作
        epsilon = EPS_END + (EPS_START - EPS_END) * \
            math.exp(-1. * agent.steps_done / EPS_DECAY)
        action = agent.select_action(state, epsilon)

        # 执行动作并获取下一状态、奖励、是否结束
        next_state, reward, done, _ = env.step(action)

        # 将状态转换为张量
        state = torch.FloatTensor([state])
        next_state = torch.FloatTensor([next_state])

        # 将状态转换存储到经验回放内存中
        agent.memory.push(state, action, next_state, reward)

        # 更新网络
        agent.optimize_model()

        # 更新状态和总奖励
        state = next_state.numpy()[0]
        total_reward += reward

        if done:
            break

    # 更新目标网络
    if i_episode % TARGET_UPDATE == 0:
        agent.update_target_model()

    # 打印训练结果
    print(f'Episode {i_episode}, Total Reward: {total_reward}')

请注意，此代码仅供参考，并且可能需要根据您的具体需求进行修改。