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，PPO算法是自己设计的④gym版本为0.28.0，pendulum-v0环境⑤算法可以直接运行，得到收敛结果

以下是一个使用Python 3.7、PyTorch 1.7.1、gym 0.28.0和PPO算法在`Pendulum-v0`环境中运行并显示收敛结果的基本示例。这个版本的PPO算法实现了连续动作空间下的优化。请注意，实际运行时可能需要进一步优化和调试。

import gym
import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions import Normal
from collections import deque
import matplotlib.pyplot as plt

# 设置环境
env = gym.make('Pendulum-v0')
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]

# 定义Actor-Critic模型
class ActorCritic(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_units=[64, 64]):
        super(ActorCritic, self).__init__()
        self.actor = nn.Sequential(
            nn.Linear(state_dim, hidden_units[0]),
            nn.Tanh(),
            nn.Linear(hidden_units[0], hidden_units[1]),
            nn.Tanh(),
            nn.Linear(hidden_units[1], action_dim),
            nn.Tanh()  # 对于连续动作，我们通常限制输出范围
        )
        self.critic = nn.Sequential(
            nn.Linear(state_dim, hidden_units[0]),
            nn.Tanh(),
            nn.Linear(hidden_units[0], hidden_units[1]),
            nn.Tanh(),
            nn.Linear(hidden_units[1], 1)
        )

    def forward(self, state):
        actor_out = self.actor(state)
        critic_out = self.critic(state)
        return actor_out, critic_out

model = ActorCritic(state_dim, action_dim)
optimizer = optim.Adam(model.parameters(), lr=3e-4)

# PPO参数
epsilon = 0.2
gamma = 0.99
K_epochs = 5
alpha = 0.97

replay_buffer = deque(maxlen=10000)
steps_per_epoch = 1000
num_episodes = 1000
plot_every = 100

# 训练过程
for ep in range(num_episodes):
    state = env.reset()
    done = False
    epoch_reward = 0
    steps_in_epoch = 0

    while not done:
        steps_in_epoch += 1
        with torch.no_grad():
            state_tensor = torch.from_numpy(state).float().unsqueeze(0)
            action_dist, _ = model(state_tensor)
            action = action_dist.mean

        next_state, reward, done, _ = env.step(np.clip(action.item(), -2, 2))  # 剪裁动作到合法范围
        next_state_tensor = torch.from_numpy(next_state).float().unsqueeze(0)

        replay_buffer.append((state, action, reward, next_state, done))

        if len(replay_buffer) == steps_per_epoch:
            # 执行一个训练周期
            for _ in range(K_epochs):
                mini_batch = random.sample(replay_buffer, batch_size)
                states, actions, rewards, next_states, dones = zip(*mini_batch)

                values, next_values = model(next_states)
                _, old_log_probs = model(states)
                target_values = rewards + gamma * (1 - dones) * next_values

                advantages = target_values - values

                ratio = torch.exp(old_log_probs - new_log_probs)
                surr1 = ratio * advantages
                surr2 = torch.clamp(ratio, 1 - epsilon, 1 + epsilon) * advantages

                actor_loss = -torch.min(surr1, surr2).mean()
                critic_loss = ((values - target_values)**2).mean()

                optimizer.zero_grad()
                actor_loss.backward()
                critic_loss.backward()
                optimizer.step()

        state = next_state
        epoch_reward += reward

    print(f"Episode {ep+1} finished with reward: {epoch_reward}")
    if ep % plot_every == 0:
        plot_learning_curve(ep, epoch_reward)

# 结束后绘制学习曲线
def plot_learning_curve(ep, epoch_reward):
    plt.plot(range(ep, ep + plot_every), [ep_reward for ep_reward in epoch_rewards[-plot_every:]])
    plt.xlabel("Episodes")
    plt.ylabel("Cumulative Reward")
    plt.title("Learning Curve")
    plt.show()

在这个例子中，`plot_learning_curve`函数用于可视化的学习过程。当程序运行结束后，你会看到一个学习曲线图。如果PPO算法成功收敛，那么曲线会稳定在一个较高的奖励水平上。