def behavior_spec_from_proto(brain_param_proto: BrainParametersProto,
agent_info: AgentInfoProto) -> BehaviorSpec:
"""
Converts brain parameter and agent info proto to BehaviorSpec object.
:param brain_param_proto: protobuf object.
:param agent_info: protobuf object.
:return: BehaviorSpec object.
"""
observation_specs = []
for obs in agent_info.observations:
observation_specs.append(
ObservationSpec(
tuple(obs.shape),
tuple(
DimensionProperty(dim)
for dim in obs.dimension_properties),
ObservationType(obs.observation_type),
))
# proto from communicator < v1.3 does not set action spec, use deprecated fields instead
if (brain_param_proto.action_spec.num_continuous_actions == 0
and brain_param_proto.action_spec.num_discrete_actions == 0):
if brain_param_proto.vector_action_space_type_deprecated == 1:
action_spec = ActionSpec(
brain_param_proto.vector_action_size_deprecated[0], ())
else:
action_spec = ActionSpec(
0, tuple(brain_param_proto.vector_action_size_deprecated))
else:
action_spec_proto = brain_param_proto.action_spec
action_spec = ActionSpec(
action_spec_proto.num_continuous_actions,
tuple(branch
for branch in action_spec_proto.discrete_branch_sizes),
)
return BehaviorSpec(observation_specs, action_spec)
def __init__(self, env_file, time_scale=10., no_graphics=False):
self.env = self._create_env(env_file, time_scale, no_graphics)
# Reset to get behavior names
self.env.reset()
self.behavior_name = list(self.env.behavior_specs)[0]
self.observation_space = ObservationSpec(640, None, None)
self.action_space = ActionSpec(continuous_size=90, discrete_branches=())
def create_observation_specs_with_shapes(
shapes: List[Tuple[int, ...]]) -> List[ObservationSpec]:
obs_specs: List[ObservationSpec] = []
for shape in shapes:
dim_prop = (DimensionProperty.UNSPECIFIED, ) * len(shape)
spec = ObservationSpec(shape, dim_prop, ObservationType.DEFAULT)
obs_specs.append(spec)
return obs_specs
def create_observation_specs_with_shapes(
shapes: List[Tuple[int, ...]]) -> List[ObservationSpec]:
obs_specs: List[ObservationSpec] = []
for i, shape in enumerate(shapes):
dim_prop = (DimensionProperty.UNSPECIFIED, ) * len(shape)
if len(shape) == 2:
dim_prop = (DimensionProperty.VARIABLE_SIZE,
DimensionProperty.NONE)
spec = ObservationSpec(
name=f"observation {i} with shape {shape}",
shape=shape,
dimension_property=dim_prop,
observation_type=ObservationType.DEFAULT,
)
obs_specs.append(spec)
return obs_specs
def _make_observation_specs(self) -> List[ObservationSpec]:
obs_shape: List[Any] = []
for _ in range(self.num_vector):
obs_shape.append((self.vec_obs_size, ))
for _ in range(self.num_visual):
obs_shape.append(self.vis_obs_size)
for _ in range(self.num_var_len):
obs_shape.append(self.var_len_obs_size)
obs_spec = create_observation_specs_with_shapes(obs_shape)
if self.goal_indices is not None:
for i in range(len(obs_spec)):
if i in self.goal_indices:
obs_spec[i] = ObservationSpec(
shape=obs_spec[i].shape,
dimension_property=obs_spec[i].dimension_property,
observation_type=ObservationType.GOAL,
name=obs_spec[i].name,
)
return obs_spec
dynamic_axes.update({'continuous_actions': {0: "batch"}})
dynamic_axes.update({'action': {0: "batch"}})
torch.onnx.export(network,
dummy_input,
EXPORT_FILE,
opset_version=9,
input_names=input_names,
output_names=output_names,
dynamic_axes=dynamic_axes)
if __name__ == '__main__':
obs_spec = [
ObservationSpec(shape=(16, ),
dimension_property=(DimensionProperty.UNSPECIFIED, ),
observation_type=ObservationType.DEFAULT)
]
act_spec = ActionSpec(continuous_size=4, discrete_branches=())
net_settings = NetworkSettings(normalize=False,
hidden_units=256,
num_layers=2,
vis_encode_type=EncoderType.SIMPLE,
memory=NetworkSettings.MemorySettings(
sequence_length=64, memory_size=256))
network = SerializableSimpleActor(obs_spec, net_settings, act_spec)
state_dict = torch.load(MODEL_FILE, map_location=torch.device('cpu'))
filtered_sd = {
i: j
for i, j in state_dict['Policy'].items() if 'critic' not in i