def __init__(self, input_width, input_height, input_channels, output_dim=128):
super().__init__()
self._output_dim = output_dim
self.conv1 = nn.Conv2d(
in_channels=input_channels,
out_channels=8,
kernel_size=(8, 8),
stride=4
)
self.conv2 = nn.Conv2d(
in_channels=8,
out_channels=16,
kernel_size=(4, 4),
stride=2
)
self.final_width = net_util.convolutional_layer_series(input_width, [
(8, 0, 4),
(4, 0, 2),
])
self.final_height = net_util.convolutional_layer_series(input_height, [
(8, 0, 4),
(4, 0, 2),
])
self.linear_layer = nn.Linear(
self.final_width * self.final_height * 16,
self.output_dim
)
def __init__(self,
input_width,
input_height,
input_channels,
output_dim=512):
super().__init__()
self._output_dim = output_dim
self.conv1 = nn.Conv2d(in_channels=input_channels,
out_channels=32,
kernel_size=(8, 8),
stride=4)
self.conv2 = nn.Conv2d(in_channels=32,
out_channels=64,
kernel_size=(4, 4),
stride=2)
self.conv3 = nn.Conv2d(in_channels=64,
out_channels=64,
kernel_size=(3, 3),
stride=1)
self.final_width = net_util.convolutional_layer_series(
input_width, [(8, 0, 4), (4, 0, 2), (3, 0, 1)])
self.final_height = net_util.convolutional_layer_series(
input_height, [(8, 0, 4), (4, 0, 2), (3, 0, 1)])
self.linear_layer = nn.Linear(
self.final_width * self.final_height *
64, # 64 is the number of channels of the last conv layer
self.output_dim)
def __init__(self, input_width, input_height, input_channels, output_dim=512):
super().__init__()
self._output_dim = output_dim
# self.conv1 = nn.Conv2d(
# in_channels=input_channels,
# out_channels=32,
# kernel_size=(8, 8),
# stride=2
# )
self.conv1 = CoordConv(x_dim=133, y_dim=133, with_r=False,
in_channels=input_channels+2,
out_channels=32,
kernel_size=(8, 8),
stride=2
)
self.conv2 = nn.Conv2d(
in_channels=32,
out_channels=64,
kernel_size=(4, 4),
stride=2
)
self.conv3 = nn.Conv2d(
in_channels=64,
out_channels=64,
kernel_size=(3, 3),
stride=1
)
self.linear1 = nn.Linear(5, 1024)
self.linear2 = nn.Linear(1024, 512)
self.final_width = net_util.convolutional_layer_series(input_width, [
(8, 0, 2),
(4, 0, 2),
(3, 0, 1)
])
self.final_height = net_util.convolutional_layer_series(input_height, [
(8, 0, 2),
(4, 0, 2),
(3, 0, 1)
])
self.linear_layer1 = nn.Linear(
self.final_width * self.final_height * 64*1 + 512, # 64 is the number of channels of the last conv layer
1024
)
self.linear_layer2 = nn.Linear(1024, self.output_dim)
def __init__(self,
input_width,
input_height,
input_channels,
output_dim=512,
initial_std_dev=0.4,
factorized_noise=True):
super().__init__()
self._output_dim = output_dim
self.conv1 = nn.Conv2d(in_channels=input_channels,
out_channels=32,
kernel_size=(8, 8),
stride=4)
self.conv2 = nn.Conv2d(in_channels=32,
out_channels=64,
kernel_size=(4, 4),
stride=2)
self.conv3 = nn.Conv2d(in_channels=64,
out_channels=64,
kernel_size=(3, 3),
stride=1)
self.final_width = net_util.convolutional_layer_series(
input_width, [(8, 0, 4), (4, 0, 2), (3, 0, 1)])
self.final_height = net_util.convolutional_layer_series(
input_height, [(8, 0, 4), (4, 0, 2), (3, 0, 1)])
self.linear_layer_one = NoisyLinear(
# 64 is the number of channels of the last conv layer
self.final_width * self.final_height * 64,
self.output_dim,
initial_std_dev=initial_std_dev,
factorized_noise=factorized_noise)
self.linear_layer_two = NoisyLinear(
# 64 is the number of channels of the last conv layer
self.final_width * self.final_height * 64,
self.output_dim,
initial_std_dev=initial_std_dev,
factorized_noise=factorized_noise)
