def group_spec_to_brain_parameters(
name: str, group_spec
) -> BrainParameters:
vec_size = np.sum(
[shape[0] for shape in group_spec['observation_shapes'] if len(shape) == 1]
)
vis_sizes = [shape for shape in group_spec['observation_shapes'] if len(shape) == 3]
cam_res = [CameraResolution(s[0], s[1], s[2]) for s in vis_sizes]
a_size: List[int] = []
if group_spec['action_type'] == 'DISCRETE':
a_size += list(group_spec['action_shape'])
vector_action_space_type = 0
else:
a_size += [group_spec.action_size]
vector_action_space_type = 1
return BrainParameters(
brain_name=name, vector_observation_space_size=int(vec_size), camera_resolutions=cam_res,
vector_action_space_size=a_size, vector_action_descriptions=[],
vector_action_space_type=vector_action_space_type
)
def create_mock_brainparams(
number_visual_observations=0,
vector_action_space_type="continuous",
vector_observation_space_size=3,
vector_action_space_size=None,
):
"""
Creates a mock BrainParameters object with parameters.
"""
# Avoid using mutable object as default param
if vector_action_space_size is None:
vector_action_space_size = [2]
mock_brain = mock.Mock()
mock_brain.return_value.number_visual_observations = number_visual_observations
mock_brain.return_value.vector_action_space_type = vector_action_space_type
mock_brain.return_value.vector_observation_space_size = (
vector_observation_space_size
)
camrez = CameraResolution(height=84, width=84, num_channels=3)
mock_brain.return_value.camera_resolutions = [camrez] * number_visual_observations
mock_brain.return_value.vector_action_space_size = vector_action_space_size
mock_brain.return_value.brain_name = "MockBrain"
return mock_brain()