|
|
|
|
|
|
) |
|
|
|
visual_encoders.append(encoded_visual) |
|
|
|
hidden_visual = tf.concat(visual_encoders, axis=1) |
|
|
|
if vector_in is not None: |
|
|
|
if vector_in.get_shape()[-1] > 0: # Don't encode 0-shape inputs |
|
|
|
hidden_state = LearningModel.create_vector_observation_encoder( |
|
|
|
vector_observation_input, |
|
|
|
h_size, |
|
|
|