代码

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import gym
import random
from collections import deque

# 定义 Actor 网络
class Actor(nn.Module):
    def __init__(self, state_dim, action_dim, max_action):
        super(Actor, self).__init__()
        self.fc1 = nn.Linear(state_dim, 256)
        self.fc2 = nn.Linear(256, 256)
        self.fc3 = nn.Linear(256, action_dim)
        self.max_action = max_action

    def forward(self, state):
        x = torch.relu(self.fc1(state))
        x = torch.relu(self.fc2(x))
        action = self.max_action * torch.tanh(self.fc3(x))  # 输出在 [-max_action, max_action] 范围内
        return action

# 定义 Critic 网络
class Critic(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(Critic, self).__init__()
        self.fc1 = nn.Linear(state_dim + action_dim, 256)
        self.fc2 = nn.Linear(256, 256)
        self.fc3 = nn.Linear(256, 1)

    def forward(self, state, action):
        x = torch.cat([state, action], 1)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        q_value = self.fc3(x)
        return q_value

# DDPG 算法
class DDPG:
    def __init__(self, state_dim, action_dim, max_action):
        self.actor = Actor(state_dim, action_dim, max_action).to(device)
        self.actor_target = Actor(state_dim, action_dim, max_action).to(device)
        self.actor_target.load_state_dict(self.actor.state_dict())
        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=1e-3)

        self.critic = Critic(state_dim, action_dim).to(device)
        self.critic_target = Critic(state_dim, action_dim).to(device)
        self.critic_target.load_state_dict(self.critic.state_dict())
        self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=1e-3)

        self.max_action = max_action
        self.replay_buffer = deque(maxlen=1000000)
        self.batch_size = 256
        self.gamma = 0.99
        self.tau = 0.005

    def select_action(self, state):
        state = torch.FloatTensor(state.reshape(1, -1)).to(device)
        return self.actor(state).cpu().data.numpy().flatten()

    def add_to_buffer(self, state, action, reward, next_state, done):
        self.replay_buffer.append((state, action, reward, next_state, done))

    def train(self):
        if len(self.replay_buffer) < self.batch_size:
            return

        # 从 Replay Buffer 中采样
        batch = random.sample(self.replay_buffer, self.batch_size)
        state, action, reward, next_state, done = map(np.stack, zip(*batch))

        state = torch.FloatTensor(state).to(device)
        action = torch.FloatTensor(action).to(device)
        reward = torch.FloatTensor(reward).unsqueeze(1).to(device)
        next_state = torch.FloatTensor(next_state).to(device)
        done = torch.FloatTensor(done).unsqueeze(1).to(device)

        # 更新 Critic 网络
        with torch.no_grad():
            next_action = self.actor_target(next_state)
            target_q = self.critic_target(next_state, next_action)
            target_q = reward + (1 - done) * self.gamma * target_q

        current_q = self.critic(state, action)
        critic_loss = nn.MSELoss()(current_q, target_q)

        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()

        # 更新 Actor 网络
        actor_loss = -self.critic(state, self.actor(state)).mean()

        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

        # 软更新目标网络
        for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

        for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

# 训练 DDPG 算法
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
env = gym.make('MountainCarContinuous-v0')  # 使用 MountainCarContinuous-v0 环境
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
max_action = float(env.action_space.high[0])

ddpg = DDPG(state_dim, action_dim, max_action)
max_episodes = 1000
max_steps = 200

for episode in range(max_episodes):
    state = env.reset()
    episode_reward = 0

    for step in range(max_steps):
        env.render()  # 渲染环境
        action = ddpg.select_action(state)
        next_state, reward, done, _ = env.step(action)
        ddpg.add_to_buffer(state, action, reward, next_state, done)
        state = next_state
        episode_reward += reward

        ddpg.train()

        if done:
            break

    print(f"Episode {episode + 1}, Reward: {episode_reward}")

env.close()

简介

在强化学习领域中，很多实际问题涉及到连续的动作空间，比如机器人的关节角度控制、自动驾驶中车辆的速度和转向控制等。传统的基于策略梯度的算法（如 A2C、A3C 等）以及基于值函数的算法（如 DQN 及其变体）在处理连续动作空间时往往面临诸多困难，DDPG 旨在有效地解决在连续动作空间下进行策略学习的问题。

图片