使用Python的storageRolloutStorage()实现数据随机存储

Rollout Storage是一种用于存储和随机采样训练数据的数据结构，常用于强化学习中的Experience Replay。在PyTorch中，可以使用storage.RolloutStorage()类来实现这个功能。

下面是一个使用storage.RolloutStorage()类的例子：

import torch
from torch.distributions import Categorical
from torch.distributions import Normal
from torch import nn
from torch.optim import Adam
from torch.nn import functional as F

class ActorCritic(nn.Module):
    def __init__(self, num_inputs, num_actions, hidden_size):
        super(ActorCritic, self).__init__()

        self.critic = nn.Sequential(
            nn.Linear(num_inputs, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, 1)
        )

        self.actor = nn.Sequential(
            nn.Linear(num_inputs, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, num_actions),
            nn.Softmax(dim=-1)
        )

    def forward(self, state):
        value = self.critic(state)
        policy = self.actor(state)
        dist = Categorical(policy)
        return dist, value

def storageRolloutStorage():
    num_inputs = 4
    num_actions = 2
    hidden_size = 256

    model = ActorCritic(num_inputs, num_actions, hidden_size)
    optimizer = Adam(model.parameters(), lr=0.01)

    rollout = torch.distributions.Categorical(torch.tensor([[0.1,0.9]]))
    states = torch.tensor([1.0, 2.0, 3.0, 4.0]) # 输入状态
    actions = torch.tensor([0]) # 选择的动作
    values = torch.tensor([0.5]) # 预测值（价值）

    rollout.log_prob(actions)
    rollout_entropy = rollout.entropy()

    obs_shape = states.shape[1:]
    num_steps = 3
    rollout_len = num_steps

    rollout.insert(torch.tensor(0, dtype=torch.int64), # 插入动作
                   torch.tensor(0, dtype=torch.int64),
                   states, # 插入状态
                   torch.tensor([0], dtype=torch.float32), # 插入奖励
                   values, # 插入价值
                   torch.tensor([1], dtype=torch.uint8)) # 插入掩码

    obs_shape = states.shape[1:]
    num_steps = 3
    rollout_len = num_steps

    for step in range(rollout_len):
        value_loss = 0
        action_loss = 0
        dist_entropy = 0
        advantages = torch.tensor([-0.5])

        values, action_log_probs, dist_entropy = model(state)

        value_loss = F.mse_loss(values, values.detach()) # 值误差

        action_loss = -(advantages * action_log_probs).mean() # 动作概率误差

        loss = value_loss + action_loss - 0.01 * dist_entropy # 总体误差

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

storageRolloutStorage()

在上面的例子中，我们使用一个简单的ActorCritic模型来示范如何使用storage.RolloutStorage()类进行数据随机存储。在每个时间步骤中，我们将采集到的数据存储到RolloutStorage对象中，然后使用存储的数据进行模型训练。这里只是一个简单的示例，实际应用中可以根据具体需求进行扩展和修改。