使用强化学习优化unet训练的学习率pytorch代码

以下是使用强化学习优化UNet训练的学习率的PyTorch代码，包括深度强化学习部分和UNet训练部分：

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

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

class Actor(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(Actor, self).__init__()
        self.fc1 = nn.Linear(state_dim, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, action_dim)
    
    def forward(self, state):
        x = torch.relu(self.fc1(state))
        x = torch.relu(self.fc2(x))
        action_prob = torch.softmax(self.fc3(x), dim=-1)
        return action_prob

class Critic(nn.Module):
    def __init__(self, state_dim):
        super(Critic, self).__init__()
        self.fc1 = nn.Linear(state_dim, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 1)
    
    def forward(self, state):
        x = torch.relu(self.fc1(state))
        x = torch.relu(self.fc2(x))
        value = self.fc3(x)
        return value

class UNet(nn.Module):
    def __init__(self):
        super(UNet, self).__init__()
        # define UNet layers
        
    def forward(self, x):
        # perform UNet forward pass
        return out

# define hyperparameters
state_dim = 10
action_dim = 1
gamma = 0.99
eps = np.finfo(np.float32).eps.item()
actor_lr = 0.001
critic_lr = 0.001
num_episodes = 1000
batch_size = 32

# create actor, critic, and UNet models
actor = Actor(state_dim, action_dim).to(device)
critic = Critic(state_dim).to(device)
unet = UNet().to(device)

# define optimizer for actor and critic
actor_optimizer = optim.Adam(actor.parameters(), lr=actor_lr)
critic_optimizer = optim.Adam(critic.parameters(), lr=critic_lr)

# define environment
env = gym.make('CartPole-v0')

# start training
for i_episode in range(num_episodes):
    state = env.reset()
    done = False
    total_reward = 0
    while not done:
        # perform UNet forward pass on state to get learning rate
        lr = unet(torch.from_numpy(state).float().to(device)).item()
        
        # sample action from actor
        action_prob = actor(torch.from_numpy(state).float().to(device))
        action_dist = torch.distributions.Categorical(action_prob)
        action = action_dist.sample()
        
        # perform action and observe next state and reward
        next_state, reward, done, _ = env.step(action.item())
        
        # calculate TD error and update critic
        value = critic(torch.from_numpy(state).float().to(device))
        next_value = critic(torch.from_numpy(next_state).float().to(device))
        td_error = reward + gamma * next_value.item() * (1 - int(done)) - value.item()
        critic_loss = td_error**2
        critic_optimizer.zero_grad()
        critic_loss.backward()
        critic_optimizer.step()
        
        # calculate actor loss and update actor
        advantage = td_error.detach()
        actor_loss = -action_dist.log_prob(action) * advantage
        actor_optimizer.zero_grad()
        actor_loss.backward()
        actor_optimizer.step()
        
        # perform UNet backward pass to update weights
        unet_optimizer = optim.Adam(unet.parameters(), lr=lr)
        unet_loss = critic_loss
        unet_optimizer.zero_grad()
        unet_loss.backward()
        unet_optimizer.step()
        
        # update state, total reward, and time step
        state = next_state
        total_reward += reward
    
    # print episode statistics
    print("Episode {}: Total Reward = {}".format(i_episode+1, total_reward))