def __init__(
self,
observation_shapes: List[Tuple[int, ...]],
network_settings: NetworkSettings,
encoded_act_size: int = 0,
):
super().__init__()
self.normalize = network_settings.normalize
self.use_lstm = network_settings.memory is not None
self.h_size = network_settings.hidden_units
self.m_size = (
network_settings.memory.memory_size
if network_settings.memory is not None
else 0
)
self.visual_encoders, self.vector_encoders = ModelUtils.create_encoders(
observation_shapes,
self.h_size,
network_settings.num_layers,
network_settings.vis_encode_type,
unnormalized_inputs=encoded_act_size,
normalize=self.normalize,
)
if self.use_lstm:
self.lstm = lstm_layer(self.h_size, self.m_size // 2, batch_first=True)
else:
self.lstm = None
def test_create_encoders(encoder_type, normalize, num_vector, num_visual,
unnormalized_inputs):
vec_obs_shape = (5, )
vis_obs_shape = (84, 84, 3)
obs_shapes = []
for _ in range(num_vector):
obs_shapes.append(vec_obs_shape)
for _ in range(num_visual):
obs_shapes.append(vis_obs_shape)
h_size = 128
num_layers = 3
unnormalized_inputs = 1
vis_enc, vec_enc = ModelUtils.create_encoders(obs_shapes, h_size,
num_layers, encoder_type,
unnormalized_inputs,
normalize)
vec_enc = list(vec_enc)
vis_enc = list(vis_enc)
assert len(vec_enc) == (1 if unnormalized_inputs + num_vector > 0 else 0
) # There's always at most one vector encoder.
assert len(vis_enc) == num_visual
if unnormalized_inputs > 0:
assert isinstance(vec_enc[0], VectorAndUnnormalizedInputEncoder)
elif num_vector > 0:
assert isinstance(vec_enc[0], VectorEncoder)
for enc in vis_enc:
assert isinstance(enc, ModelUtils.get_encoder_for_type(encoder_type))
