用pytorch实现一下基于策略梯度下降的深度强化学习算法

好的，下面是一个基于策略梯度下降的深度强化学习算法的PyTorch实现。

首先我们需要导入相关的包和定义一些超参数：

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

# 超参数
learning_rate = 0.01
gamma = 0.99
hidden_layer = 128
batch_size = 32
episodes = 1000
render = False

接下来，我们需要定义一个神经网络来估计策略。这里我们使用一个简单的全连接神经网络，输入是状态向量，输出是动作的概率分布。

class Policy(nn.Module):
    def __init__(self, input_dim, output_dim, hidden_layer=128):
        super(Policy, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_layer)
        self.fc2 = nn.Linear(hidden_layer, output_dim)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.softmax(self.fc2(x), dim=1)
        return x

接下来，我们定义一些函数来计算策略梯度和更新策略。

def compute_policy_gradient(policy, rewards, states, actions):
    # 计算策略梯度
    R = 0
    policy_gradient = []
    for r in rewards[::-1]:
        R = r + gamma * R
        policy_gradient.insert(0, -R)
    policy_gradient = torch.tensor(policy_gradient)
    policy_gradient = (policy_gradient - policy_gradient.mean()) / (policy_gradient.std() + 1e-9)
    
    # 计算动作概率分布
    states = torch.tensor(states, dtype=torch.float32)
    action_probs = policy(states)
    dist = torch.distributions.Categorical(action_probs)
    
    # 计算策略梯度乘以动作概率
    actions = torch.tensor(actions)
    log_probs = dist.log_prob(actions)
    policy_gradient *= log_probs.exp()
    
    return policy_gradient

def update_policy(policy, optimizer, policy_gradient):
    # 更新策略
    optimizer.zero_grad()
    policy_gradient.mean().backward()
    optimizer.step()

最后，我们定义主函数来运行整个强化学习算法。在每个回合结束后，我们先计算策略梯度，然后用策略梯度更新策略。

if __name__ == '__main__':
    # 初始化环境和策略
    env = gym.make('CartPole-v0')
    policy = Policy(env.observation_space.shape[0], env.action_space.n, hidden_layer=hidden_layer)
    optimizer = optim.Adam(policy.parameters(), lr=learning_rate)

    # 训练策略
    for episode in range(episodes):
        # 初始化回合
        state = env.reset()
        done = False
        rewards = []
        states = []
        actions = []

        # 运行回合
        while not done:
            if render:
                env.render()

            # 选择动作
            state = torch.tensor(state, dtype=torch.float32).unsqueeze(0)
            action_probs = policy(state)
            dist = torch.distributions.Categorical(action_probs)
            action = dist.sample().item()

            # 执行动作
            next_state, reward, done, _ = env.step(action)

            # 存储数据
            states.append(state)
            actions.append(action)
            rewards.append(reward)

            # 更新状态
            state = next_state

        # 计算策略梯度并更新策略
        policy_gradient = compute_policy_gradient(policy, rewards, states, actions)
        update_policy(policy, optimizer, policy_gradient)

        # 打印训练进度
        if episode % 10 == 0:
            print(f'Episode {episode}/{episodes}')
            
    env.close()

这样我们就完成了基于策略梯度下降的深度强化学习算法的PyTorch实现。您可以根据实际情况调整超参数和神经网络结构来获得更好的性能。