写一段强化学习代码,要求如下:①python3.7解释器②pytorch版本为1.7.1③PPO算法且输出为连续值④PPO程序是自己设计的
时间: 2024-09-17 18:09:35 浏览: 69
在Python 3.7环境中,使用PyTorch 1.7.1实现Proximal Policy Optimization (PPO)算法的一个基本框架通常会包含以下几个步骤:
```python
import torch
from torch import nn, optim
import torch.nn.functional as F
# 定义模型(这里是Actor-Critic架构)
class ActorCritic(nn.Module):
def __init__(self, state_dim, action_dim, hidden_size=64):
super(ActorCritic, self).__init__()
self.actor = nn.Sequential(
nn.Linear(state_dim, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, action_dim)
)
self.critic = nn.Sequential(
nn.Linear(state_dim, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, 1)
)
def forward(self, x):
actor_out = self.actor(x)
critic_out = self.critic(x)
return actor_out, critic_out
# 初始化网络、优化器和策略梯度更新参数
state_dim, action_dim = ... # 根据实际环境填充
model = ActorCritic(state_dim, action_dim)
optimizer = optim.Adam(model.parameters(), lr=3e-4)
def ppo_update(data):
states, actions, log_probs, advantages = data
values = model.critic(states).squeeze(-1)
old_policy_dist = model.actor(states)
# 计算新分布
new_policy_dist, _ = model.actor(states)
ratio = torch.exp(torch.log(new_policy_dist) - torch.log(old_policy_dist))
surr1 = ratio * advantages
surr2 = torch.clamp(ratio, 1 - clip_param, 1 + clip_param) * advantages
actor_loss = -torch.min(surr1, surr2).mean()
critic_loss = F.mse_loss(values, advantages.detach()).mean()
optimizer.zero_grad()
(actor_loss + critic_loss).backward()
optimizer.step()
# PPO训练循环
clip_param = 0.2 # 这里假设clip_param是常量,可以根据实际情况调整
num_steps = ... # 总步数
for num_updates in range(num_total_updates): # 更新次数
for i in range(num_steps):
# 获取经验数据
experiences = collect_experience() # 自定义函数获取采样数据
# 执行PPO更新
ppo_update(experiences)
# 输出性能指标或其他信息
print(f"Update {num_updates+1}, Average Return: {compute_average_return()}")
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