def __init__(self, input_shape, num_actions, activation, hparams):
super(Network, self).__init__()
self.input_shape = input_shape
self.num_actions = num_actions
self.act = ACTS[activation]
self.noisy = hparams.noisy
if self.noisy:
self.layers = nn.Sequential(nn.Linear(input_shape, 128),
self.act(), NoisyLinear(128, 128),
self.act(),
NoisyLinear(128, num_actions))
else:
self.layers = nn.Sequential(nn.Linear(input_shape,
128), self.act(),
nn.Linear(128, 128), self.act(),
nn.Linear(128, num_actions))
def __init__(self, input_shape, num_actions, activation, hparams):
super(CnnNetwork, self).__init__()
self.input_shape = input_shape
self.num_actions = num_actions
self.act = ACTS[activation]
self.noisy = hparams.noisy
self.features = nn.Sequential(
nn.Conv2d(input_shape[0], 32, kernel_size=8, stride=4), self.act(),
nn.Conv2d(32, 64, kernel_size=4, stride=2), self.act(),
nn.Conv2d(64, 64, kernel_size=3, stride=1), self.act())
if self.noisy:
self.fc = nn.Sequential(NoisyLinear(self.feature_size(), 512),
self.act(),
NoisyLinear(512, self.num_actions))
else:
self.fc = nn.Sequential(nn.Linear(self.feature_size(), 512),
self.act(),
nn.Linear(512, self.num_actions))