Pytorch: weights not updating, using dual network

Question

I hope you can help. I am designing a controller for a system, where the controller NN is trained using an imported NN I made, which is the state space representation of the system.

But it seems the weights are not updating on each epoch, I have attached the code below:

This is my defined controller:

class Net(nn.Module):
    
    def __init__(self, n1, n2, n3):
        super().__init__()
        self.fc1 = nn.Linear((10 * 2), n1)
        self.fc2 = nn.Linear(n1, n2)
        self.fc3 = nn.Linear(n2, n3)
        self.fc4 = nn.Linear(n3, 1)
    
    def forward(self, x):
        x = F.gelu(self.fc1(x))
        x = F.gelu(self.fc2(x))
        x = F.gelu(self.fc3(x))
        x = F.gelu(self.fc4(x))
        return x

below is my training loop:

LR = 0.001
w_decay = 0.005
optimizer = optim.SGD(dpc.parameters(), lr=LR, weight_decay=w_decay)
EPOCHS = 50
batch = 2
print(‘Training network…’)
dpc.train(mode=True)

for epoch in range(EPOCHS):
    for dd in range(0, len(x_train) - batch):
        X = x_train[dd:dd + batch]
        optimizer.zero_grad()
        output = dpc(X)
        input_ssm = X.detach().numpy()
        input_ssm = input_ssm.tolist()
        input_ssm[0].append(output.detach().numpy().tolist()[0][0])
        input_ssm[1].append(output.detach().numpy().tolist()[1][0])
        input_ssm = np.array(input_ssm)
        input_ssm = torch.Tensor(input_ssm)
        y = ss_model(input_ssm)
        y = (y*(max_a[1][0] - min_a[1][0])) + min_a[1][0]
        desired = np.array([[0], [0]])
        desired = torch.Tensor(desired)
        loss = (F.mse_loss(y, desired))
        combined_loss += loss
        loss.backward()
        optimizer.step()

Many thanks.