BCQ
(generator
, gen_optim
, perturbator
, pert_optim
, critic1
, critic2
, critic_optim
, tau
=0.001
, gamma
=0.99
, lam
=0.75
, policy_delay
=1
, item_embeds
=None
, device
=device(type='cpu')
) :: Module
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))
Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:to
, etc.
:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool