def external_brains():
result = {}
for behavior_name, behavior_specs in env.behavior_specs.items():
result[behavior_name] = behavior_spec_to_brain_parameters(
behavior_name, behavior_specs
)
return result
def demo_to_buffer(
file_path: str,
sequence_length: int) -> Tuple[BrainParameters, AgentBuffer]:
"""
Loads demonstration file and uses it to fill training buffer.
:param file_path: Location of demonstration file (.demo).
:param sequence_length: Length of trajectories to fill buffer.
:return:
"""
behavior_spec, info_action_pair, _ = load_demonstration(file_path)
demo_buffer = make_demo_buffer(info_action_pair, behavior_spec,
sequence_length)
brain_params = behavior_spec_to_brain_parameters("DemoBrain",
behavior_spec)
return brain_params, demo_buffer
def external_brains():
result = {}
for brain_name in env.get_behavior_names():
result[brain_name] = behavior_spec_to_brain_parameters(
brain_name, env.get_behavior_spec(brain_name))
return result
def external_brains(self) -> Dict[BehaviorName, BrainParameters]:
result = {}
for brain_name in self.env.get_behavior_names():
result[brain_name] = behavior_spec_to_brain_parameters(
brain_name, self.env.get_behavior_spec(brain_name))
return result
def demo_to_buffer(
file_path: str, sequence_length: int, expected_brain_params: BrainParameters = None
) -> Tuple[BrainParameters, AgentBuffer]:
"""
Loads demonstration file and uses it to fill training buffer.
:param file_path: Location of demonstration file (.demo).
:param sequence_length: Length of trajectories to fill buffer.
:return:
"""
behavior_spec, info_action_pair, _ = load_demonstration(file_path)
demo_buffer = make_demo_buffer(info_action_pair, behavior_spec, sequence_length)
brain_params = behavior_spec_to_brain_parameters("DemoBrain", behavior_spec)
if expected_brain_params:
# check action dimensions in demonstration match
if (
brain_params.vector_action_space_size
!= expected_brain_params.vector_action_space_size
):
raise RuntimeError(
"The action dimensions {} in demonstration do not match the policy's {}.".format(
brain_params.vector_action_space_size,
expected_brain_params.vector_action_space_size,
)
)
# check the action types in demonstration match
if (
brain_params.vector_action_space_type
!= expected_brain_params.vector_action_space_type
):
raise RuntimeError(
"The action type of {} in demonstration do not match the policy's {}.".format(
brain_params.vector_action_space_type,
expected_brain_params.vector_action_space_type,
)
)
# check number of vector observations in demonstration match
if (
brain_params.vector_observation_space_size
!= expected_brain_params.vector_observation_space_size
):
raise RuntimeError(
"The vector observation dimensions of {} in demonstration do not match the policy's {}.".format(
brain_params.vector_observation_space_size,
expected_brain_params.vector_observation_space_size,
)
)
# check number of visual observations/resolutions in demonstration match
if (
brain_params.number_visual_observations
!= expected_brain_params.number_visual_observations
):
raise RuntimeError(
"Number of visual observations {} in demonstrations do not match the policy's {}.".format(
brain_params.number_visual_observations,
expected_brain_params.number_visual_observations,
)
)
for i, (resolution, expected_resolution) in enumerate(
zip(
brain_params.camera_resolutions,
expected_brain_params.camera_resolutions,
)
):
if resolution != expected_resolution:
raise RuntimeError(
"The resolution of visual observation {} in demonstrations do not match the policy's.".format(
i
)
)
return brain_params, demo_buffer
def external_brains(self) -> Dict[BehaviorName, BrainParameters]:
result = {}
for behavior_name, behavior_spec in self.env.behavior_specs.items():
result[behavior_name] = behavior_spec_to_brain_parameters(
behavior_name, behavior_spec)
return result
