自适应动态规划pytorch代码

自适应动态规划（Adaptive Dynamic Programming，ADP）是一种基于动态规划的增强学习方法，它通过学习环境的模型和价值函数来实现智能决策。下面是一个使用PyTorch实现自适应动态规划的简单示例代码：

import torch
import torch.nn as nn
import torch.optim as optim

class ValueFunction(nn.Module):
    def __init__(self, state_dim):
        super(ValueFunction, self).__init__()
        self.fc1 = nn.Linear(state_dim, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

class ADP:
    def __init__(self, state_dim, action_dim, learning_rate=0.001, gamma=0.9):
        self.value_function = ValueFunction(state_dim)
        self.optimizer = optim.Adam(self.value_function.parameters(), lr=learning_rate)
        self.loss_fn = nn.MSELoss()
        self.gamma = gamma

    def update_value_function(self, state, next_state, reward):
        state_value = self.value_function(state)
        next_state_value = self.value_function(next_state)
        target_value = reward + self.gamma * next_state_value
        loss = self.loss_fn(state_value, target_value)

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

    def get_action(self, state):
        with torch.no_grad():
            state_value = self.value_function(state)
            action = torch.argmax(state_value).item()
        return action

# 示例用法
state_dim = 4
action_dim = 2
adp = ADP(state_dim, action_dim)

state = torch.tensor([0.1, 0.2, 0.3, 0.4])
next_state = torch.tensor([0.2, 0.3, 0.4, 0.5])
reward = 1.0

adp.update_value_function(state, next_state, reward)
action = adp.get_action(state)

print("Action:", action)

这段代码实现了一个简单的自适应动态规划算法，其中`ValueFunction`类定义了一个值函数的神经网络模型，`ADP`类封装了自适应动态规划的相关操作，包括值函数的更新和动作的选择。在示例用法中，我们定义了状态维度为4，动作维度为2的问题，并进行了一次值函数的更新和动作的选择。