def _build_model(self, blur, brightness, need_ray, need_speed):
assert self.obs_shapes[0] == (84, 84, 3)
assert self.obs_shapes[1] == (2,) # ray
assert self.obs_shapes[2] == (8,) # vector
self.need_ray = need_ray
self.need_speed = need_speed
self.conv = m.ConvLayers(84, 84, 3, 'simple',
out_dense_n=64, out_dense_depth=2)
self.dense = m.LinearLayers(self.conv.output_size,
dense_n=64, dense_depth=1)
self.rnn = m.GRU(64 + self.c_action_size, 64, 1)
if blur != 0:
self.blurrer = m.Transform(T.GaussianBlur(blur, sigma=blur))
else:
self.blurrer = None
self.brightness = m.Transform(T.ColorJitter(brightness=(brightness, brightness)))
cropper = torch.nn.Sequential(
T.RandomCrop(size=(50, 50)),
T.Resize(size=(84, 84))
)
self.random_transformers = T.RandomChoice([
m.Transform(SaltAndPepperNoise(0.2, 0.5)),
m.Transform(GaussianNoise()),
m.Transform(T.GaussianBlur(9, sigma=9)),
m.Transform(cropper)
])
def _build_model(self):
vec_size = self.obs_shapes[1][
0] if self.use_extra_data else self.obs_shapes[1][0] - EXTRA_SIZE
self.dense = m.LinearLayers(self.state_size,
dense_n=64,
dense_depth=2,
output_size=self.obs_shapes[0][0] +
vec_size)
def _build_model(self, blur, brightness, ray_random, need_speed):
assert self.obs_shapes[0] == (84, 84, 3)
assert self.obs_shapes[1] == (1442, ) # ray (1 + 360 + 360) * 2
assert self.obs_shapes[2] == (6, ) # vector
self.ray_random = ray_random
self.need_speed = need_speed
if blur != 0:
self.blurrer = m.Transform(T.GaussianBlur(blur, sigma=blur))
else:
self.blurrer = None
self.brightness = m.Transform(
T.ColorJitter(brightness=(brightness, brightness)))
self.ray_index = []
for i in reversed(range(RAY_SIZE // 2)):
self.ray_index.append((i * 2 + 1) * 2)
self.ray_index.append((i * 2 + 1) * 2 + 1)
for i in range(RAY_SIZE // 2):
self.ray_index.append((i * 2 + 2) * 2)
self.ray_index.append((i * 2 + 2) * 2 + 1)
self.conv = m.ConvLayers(84,
84,
3,
'simple',
out_dense_n=64,
out_dense_depth=2)
self.ray_conv = m.Conv1dLayers(RAY_SIZE,
2,
'default',
out_dense_n=64,
out_dense_depth=2)
self.vis_ray_dense = m.LinearLayers(self.conv.output_size +
self.ray_conv.output_size,
dense_n=64,
dense_depth=1)
self.rnn = m.GRU(64 + self.c_action_size, 64, 1)
cropper = torch.nn.Sequential(T.RandomCrop(size=(50, 50)),
T.Resize(size=(84, 84)))
self.random_transformers = T.RandomChoice([
m.Transform(SaltAndPepperNoise(0.2, 0.5)),
m.Transform(GaussianNoise()),
m.Transform(T.GaussianBlur(9, sigma=9)),
m.Transform(cropper)
])
def _build_model(self):
assert self.obs_shapes[0] == (30, 30, 3)
assert self.obs_shapes[1] == (2, )
self.conv = m.ConvLayers(30,
30,
3,
'simple',
out_dense_n=64,
out_dense_depth=2)
self.dense = m.LinearLayers(self.conv.output_size + 2,
dense_n=64,
dense_depth=1)
def _build_model(self):
assert self.obs_shapes[0] == (30, 30, 3)
assert self.obs_shapes[1] == (6, )
self.conv = m.ConvLayers(30,
30,
3,
'simple',
out_dense_n=64,
out_dense_depth=2)
self.rnn = m.GRU(self.conv.output_size + self.c_action_size, 64, 1)
self.dense = m.LinearLayers(input_size=64 + self.obs_shapes[1][0] -
EXTRA_SIZE,
dense_n=128,
dense_depth=2,
output_size=64)
def _build_model(self):
assert self.obs_shapes[0] == (84, 84, 3)
assert self.obs_shapes[1] == (84, 84, 3)
assert self.obs_shapes[2] == (84, 84, 3)
assert self.obs_shapes[3] == (11, )
self.conv = m.ConvLayers(84,
84,
3 * 3,
'simple',
out_dense_n=64,
out_dense_depth=2)
self.dense = m.LinearLayers(self.conv.output_size + 11 - EXTRA_SIZE,
dense_n=64,
dense_depth=1)
self.rnn = m.GRU(64 + self.c_action_size, 64, 1)
def _build_model(self):
self.conv_transpose = m.ConvTransposeLayers(
self.state_size,
64,
1,
2,
2,
32,
conv_transpose=nn.Sequential(
nn.ConvTranspose2d(32, 32, 4, 2),
nn.LeakyReLU(),
nn.ConvTranspose2d(32, 16, 8, 4),
nn.LeakyReLU(),
nn.ConvTranspose2d(16, 3, 3, 1),
nn.LeakyReLU(),
))
self.vec_dense = m.LinearLayers(self.state_size,
dense_depth=2,
output_size=EXTRA_SIZE)
def _build_model(self):
self.dense = m.LinearLayers(
self.state_size, 8, 2, self.obs_shapes[0][0]
if self.use_extra_data else self.obs_shapes[0][0] - EXTRA_SIZE)
def _build_model(self):
self.dense = m.LinearLayers(self.state_size,
dense_n=64, dense_depth=2,
output_size=self.obs_shapes[0][0] + self.obs_shapes[1][0])