DDPG模块python

DDPG是一种深度强化学习算法，用于解决连续动作空间的问题。在Python中，可以使用TensorFlow或PyTorch等深度学习框架来实现DDPG模块。以下是一个使用PyTorch实现DDPG的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

class Actor(nn.Module):
    def __init__(self, state_dim, action_dim, max_action):
        super(Actor, self).__init__()
        self.layer1 = nn.Linear(state_dim, 400)
        self.layer2 = nn.Linear(400, 300)
        self.layer3 = nn.Linear(300, action_dim)
        self.max_action = max_action

    def forward(self, x):
        x = torch.relu(self.layer1(x))
        x = torch.relu(self.layer2(x))
        x = self.max_action * torch.tanh(self.layer3(x))
        return x

class Critic(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(Critic, self).__init__()
        self.layer1 = nn.Linear(state_dim + action_dim, 400)
        self.layer2 = nn.Linear(400 , 300)
        self.layer3 = nn.Linear(300, 1)

    def forward(self, x, u):
        xu = torch.cat([x, u], 1)
        x = torch.relu(self.layer1(xu))
        x = torch.relu(self.layer2(x))
        x = self.layer3(x)
        return x

class DDPG(object):
    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-4)

        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

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

    def train(self, replay_buffer, batch_size=64, discount=0.99, tau=0.005):
        state, action, next_state, reward, not_done = replay_buffer.sample(batch_size)

        state = torch.FloatTensor(state).to(device)
        action = torch.FloatTensor(action).to(device)
        next_state = torch.FloatTensor(next_state).to(device)
        reward = torch.FloatTensor(reward).to(device)
        not_done = torch.FloatTensor(1 - not_done).to(device)

        # Update critic
        next_action = self.actor_target(next_state)
        target_Q = self.critic_target(next_state, next_action)
        target_Q = reward + (not_done * discount * target_Q).detach()
        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()

        # Update actor
        pred_action = self.actor(state)
        actor_loss = -self.critic(state, pred_action).mean()
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

        # Update target networks
        for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
            target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)

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

    def save(self, filename):
        torch.save(self.actor.state_dict(), filename + "_actor")
        torch.save(self.critic.state_dict(), filename + "_critic")

    def load(self, filename):
        self.actor.load_state_dict(torch.load(filename + "_actor"))
        self.critic.load_state_dict(torch.load(filename + "_critic"))
        self.actor_target.load_state_dict(self.actor.state_dict())
        self.critic_target.load_state_dict(self.critic.state_dict())

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")