transformer和强化学习结合的代码
时间: 2023-11-06 16:58:48 浏览: 85
抱歉,我是一名语言模型,无法提供完整的代码。但是,以下是一个可能的示例,用于将Transformer和强化学习结合起来:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from transformer import Transformer
from replay_buffer import ReplayBuffer
class Agent:
def __init__(self, state_dim, action_dim, max_action):
self.state_dim = state_dim
self.action_dim = action_dim
self.max_action = max_action
self.actor = Transformer(state_dim, action_dim)
self.critic = Transformer(state_dim, 1)
self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=0.001)
self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=0.001)
self.replay_buffer = ReplayBuffer()
def select_action(self, state):
state = torch.tensor(state, dtype=torch.float32).unsqueeze(0)
action = self.actor(state)
action = action.detach().numpy()[0]
return action
def train(self, batch_size=32, gamma=0.99, tau=0.005):
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.replay_buffer.sample(batch_size)
state_batch = torch.tensor(state_batch, dtype=torch.float32)
action_batch = torch.tensor(action_batch, dtype=torch.float32)
reward_batch = torch.tensor(reward_batch, dtype=torch.float32)
next_state_batch = torch.tensor(next_state_batch, dtype=torch.float32)
done_batch = torch.tensor(done_batch, dtype=torch.float32)
with torch.no_grad():
next_action_batch = self.actor(next_state_batch)
q_next = self.critic(torch.cat((next_state_batch, next_action_batch), dim=1))
q_target = reward_batch + (1 - done_batch) * gamma * q_next
q_value = self.critic(torch.cat((state_batch, action_batch), dim=1))
critic_loss = nn.MSELoss(q_value, q_target)
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
actor_loss = -self.critic(torch.cat((state_batch, self.actor(state_batch)), dim=1)).mean()
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
for param, target_param in zip(self.actor.parameters(), self.target_actor.parameters()):
target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
for param, target_param in zip(self.critic.parameters(), self.target_critic.parameters()):
target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
def update_replay_buffer(self, state, action, reward, next_state, done):
self.replay_buffer.add(state, action, reward, next_state, done)
```
这里的 `Transformer` 是一个PyTorch类,用于实现Transformer模型,而 `ReplayBuffer` 是一个用于存储经验回放的类。在 `Agent` 类中,我们定义了用于选择动作和训练模型的方法,并且在训练过程中使用了批量更新和目标网络来提高模型的稳定性。在 `update_replay_buffer` 方法中,我们将每个步骤的经验添加到回放缓冲区中,以便在以后的训练中使用。