Python 将Pyrotch浮点模型转换为双精度

我想从健身房解cartpole。结果表明，状态是双浮点精度的，而pytorch默认情况下创建的模型是单浮点精度的

class QNetworkMLP(Module):
    def __init__(self,state_dim,num_actions):
        super(QNetworkMLP,self).__init__()
        self.l1 = Linear(state_dim,64)
        self.l2 = Linear(64,64)
        self.l3 = Linear(64,128)
        self.l4 = Linear(128,num_actions)
        self.relu = ReLU()
        self.lrelu = LeakyReLU()
    
    def forward(self,x) :
        x = self.lrelu(self.l1(x))
        x = self.lrelu(self.l2(x))
        x = self.lrelu(self.l3(x))
        x = self.l4(x)
        return x

我试着通过

model = QNetworkMLP(4,2).double()

但它仍然不起作用，我得到了同样的错误

File ".\agent.py", line 117, in update_online_network
    predicted_Qval = self.online_network(states_batch).gather(1,actions_batch)
  File "C:\Users\27abh\anaconda3\envs\gym\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\Users\27abh\Desktop\OpenAI Gym\Cartpole\agent_model.py", line 16, in forward
    x = self.lrelu(self.l1(x))
  File "C:\Users\27abh\anaconda3\envs\gym\lib\site-packages\torch\nn\modules\module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "C:\Users\27abh\anaconda3\envs\gym\lib\site-packages\torch\nn\modules\linear.py", line 91, in forward
    return F.linear(input, self.weight, self.bias)
  File "C:\Users\27abh\anaconda3\envs\gym\lib\site-packages\torch\nn\functional.py", line 1674, in linear
    ret = torch.addmm(bias, input, weight.t())
RuntimeError: Expected object of scalar type Double but got scalar type Float for argument #2 'mat1' in call to _th_addmm

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您可以在初始化模型后尝试以下操作：

 model.to(torch.double)

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不起作用，相同的错误您是否尝试过将输入的

x

转换为相应的数据类型，而不是模型？