这段代码的作用 self.actor_optimizer.zero_grad() actor_loss.backward() self.actor_optimizer.step()
时间: 2024-05-29 22:15:45 浏览: 13
这段代码用于实现Actor-Critic算法中的Actor网络的参数更新。
首先,`self.actor_optimizer.zero_grad()`将Actor网络的梯度清零,以避免梯度累加导致错误的参数更新。
接着,`actor_loss.backward()`对Actor网络的损失函数进行反向传播,计算每个参数对损失函数的梯度。
最后,`self.actor_optimizer.step()`根据梯度下降法,利用优化器对Actor网络的参数进行更新。
相关问题
多智能体编队actor-critic pytorch代码
抱歉,我是一个语言模型AI,没有实际的代码能力。但是,以下是一个多智能体编队actor-critic算法的PyTorch示例代码,供您参考:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
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 = F.relu(self.fc1(state))
x = F.relu(self.fc2(x))
x = torch.tanh(self.fc3(x))
return x
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 = F.relu(self.fc1(state))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
class A2C:
def __init__(self, state_dim, action_dim, num_agents, gamma=0.99, lr=0.001):
self.num_agents = num_agents
self.gamma = gamma
self.actor = Actor(state_dim, action_dim)
self.critic = Critic(state_dim)
self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=lr)
self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=lr)
def act(self, states):
actions = []
for state in states:
state = torch.FloatTensor(state).unsqueeze(0)
action = self.actor(state)
actions.append(action.detach().numpy().flatten())
return actions
def update(self, states, actions, rewards, next_states, dones):
states = torch.FloatTensor(states)
actions = torch.FloatTensor(actions)
rewards = torch.FloatTensor(rewards).unsqueeze(1)
next_states = torch.FloatTensor(next_states)
dones = torch.FloatTensor(dones).unsqueeze(1)
values = self.critic(states)
next_values = self.critic(next_states)
td_targets = rewards + self.gamma * next_values * (1 - dones)
td_errors = td_targets - values
actor_loss = 0
critic_loss = 0
for i in range(self.num_agents):
advantage = td_errors[i].detach()
log_prob = -torch.log(2 * np.pi * torch.ones(1, 1)) - \
torch.log(torch.FloatTensor([0.2])) - \
(actions[i] - self.actor(states[i].unsqueeze(0))) ** 2 / (2 * 0.2 ** 2)
actor_loss += -(log_prob * advantage).mean()
critic_loss += td_errors[i].pow(2).mean()
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
```
在这个代码中,我们定义了Actor和Critic两个神经网络,并用PyTorch内置的优化器Adam来更新它们的参数。在每个时间步骤中,我们使用actor网络来选择动作,然后将动作传递给环境并获得奖励和下一个状态。然后我们使用critic网络来估计当前状态的值,并计算TD误差。最后,我们使用这些TD错误来更新actor和critic网络的参数,以最大化预期回报。
Actor-Critic
Actor-Critic是一种强化学习算法,它结合了策略梯度和值函数的优点。在Actor-Critic算法中,Actor使用策略函数生成动作并与环境交互,Critic使用价值函数评估Actor的表现并指导Actor下一步的动作。Actor和Critic都是神经网络,需要进行梯度更新,互相依赖。Actor-Critic算法可以用于解决连续动作空间的问题,例如机器人控制和游戏AI等领域。
以下是一个Actor-Critic算法的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import gym
# 定义Actor神经网络
class Actor(nn.Module):
def __init__(self, input_dim, output_dim):
super(Actor, self).__init__()
self.fc1 = nn.Linear(input_dim, 128)
self.fc2 = nn.Linear(128, output_dim)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.softmax(self.fc2(x))
return x
# 定义Critic神经网络
class Critic(nn.Module):
def __init__(self, input_dim):
super(Critic, self).__init__()
self.fc1 = nn.Linear(input_dim, 128)
self.fc2 = nn.Linear(128, 1)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# 定义Actor-Critic算法
class ActorCritic:
def __init__(self, env):
self.env = env
self.obs_dim = env.observation_space.shape[0]
self.action_dim = env.action_space.n
self.actor = Actor(self.obs_dim, self.action_dim)
self.critic = Critic(self.obs_dim)
self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=0.01)
self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=0.01)
def train(self, max_episodes=1000, max_steps=1000, gamma=0.99):
for i in range(max_episodes):
obs = self.env.reset()
done = False
total_reward = 0
for t in range(max_steps):
# 选择动作
action_probs = self.actor(torch.FloatTensor(obs))
action = torch.multinomial(action_probs, 1).item()
# 执行动作
next_obs, reward, done, _ = self.env.step(action)
# 更新Critic
value = self.critic(torch.FloatTensor(obs)).item()
next_value = self.critic(torch.FloatTensor(next_obs)).item()
td_error = reward + gamma * next_value - value
critic_loss = td_error ** 2
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# 更新Actor
advantage = td_error
log_prob = torch.log(action_probs[action])
actor_loss = -log_prob * advantage
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
obs = next_obs
total_reward += reward
if done:
break
print("Episode {}: Total reward = {}".format(i, total_reward))
# 使用CartPole环境测试Actor-Critic算法
env = gym.make('CartPole-v0')
ac = ActorCritic(env)
ac.train()
```