def __init__(
self,
observation_specs: List[ObservationSpec],
h_size: int,
vis_encode_type: EncoderType,
normalize: bool = False,
):
"""
Returns an ObservationEncoder that can process and encode a set of observations.
Will use an RSA if needed for variable length observations.
"""
super().__init__()
self.processors, self.embedding_sizes = ModelUtils.create_input_processors(
observation_specs, h_size, vis_encode_type, normalize=normalize
)
self.rsa, self.x_self_encoder = ModelUtils.create_residual_self_attention(
self.processors, self.embedding_sizes, h_size
)
if self.rsa is not None:
total_enc_size = sum(self.embedding_sizes) + h_size
else:
total_enc_size = sum(self.embedding_sizes)
self.normalize = normalize
self._total_enc_size = total_enc_size
self._total_goal_enc_size = 0
self._goal_processor_indices: List[int] = []
for i in range(len(observation_specs)):
if observation_specs[i].observation_type == ObservationType.GOAL_SIGNAL:
self._total_goal_enc_size += self.embedding_sizes[i]
self._goal_processor_indices.append(i)
def __init__(
self,
observation_specs: List[ObservationSpec],
h_size: int,
vis_encode_type: EncoderType,
normalize: bool = False,
):
"""
Returns an ObservationEncoder that can process and encode a set of observations.
Will use an RSA if needed for variable length observations.
"""
super().__init__()
self.processors, self.embedding_sizes = ModelUtils.create_input_processors(
observation_specs, h_size, vis_encode_type, normalize=normalize)
self.rsa, self.x_self_encoder = ModelUtils.create_residual_self_attention(
self.processors, self.embedding_sizes, h_size)
if self.rsa is not None:
total_enc_size = sum(self.embedding_sizes) + h_size
else:
total_enc_size = sum(self.embedding_sizes)
self.normalize = normalize
self._total_enc_size = total_enc_size
