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

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：

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()}")