自适应动态规划 pytorch实现
时间: 2023-07-31 19:13:56 浏览: 53
下面是使用PyTorch实现自适应动态规划的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
# 定义状态空间和动作空间
states = [0, 1, 2, 3]
actions = [0, 1]
# 定义值函数网络
class ValueFunction(nn.Module):
def __init__(self):
super(ValueFunction, self).__init__()
self.fc1 = nn.Linear(2, 16)
self.fc2 = nn.Linear(16, 1)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# 初始化值函数网络
model = ValueFunction()
# 定义损失函数和优化器
loss_fn = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)
# 开始迭代
for i in range(100):
for state in states:
for action in actions:
# 计算当前值函数的估计值
state_action = torch.tensor([[state, action]], dtype=torch.float32)
value_estimate = model(state_action)
# 根据估计值选择下一步的动作
next_state = torch.tensor([torch.randint(4, (1,))], dtype=torch.float32)
next_action = torch.tensor([torch.randint(2, (1,))], dtype=torch.float32)
# 计算目标值
next_state_action = torch.cat([next_state, next_action], dim=1)
target = model(next_state_action)
# 计算损失函数并更新值函数
loss = loss_fn(target, value_estimate)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 输出最终的值函数估计结果
for state in states:
for action in actions:
state_action = torch.tensor([[state, action]], dtype=torch.float32)
value_estimate = model(state_action)
print(f"State: {state}, Action: {action}, Value: {value_estimate.item()}")
```
在这个示例代码中,我们使用了PyTorch构建了值函数网络,并使用Adam优化器和均方误差损失函数来更新网络的权重。在每个迭代步骤中,根据当前状态和动作计算值函数的估计值,并根据估计值选择下一步的动作。然后,计算目标值并使用反向传播算法更新网络的权重。最后输出每个状态和动作对应的值函数估计结果。你可以根据自己的问题进行相应的修改和扩展。