def initialize(self, inputs: UnityInputProto) -> UnityOutputProto:
bp = BrainParametersProto(
vector_action_size=[2],
vector_action_descriptions=["", ""],
vector_action_space_type=discrete if self.is_discrete else continuous,
brain_name=self.brain_name,
is_training=True,
)
rl_init = UnityRLInitializationOutputProto(
name="RealFakeAcademy",
version=UnityEnvironment.API_VERSION,
log_path="",
brain_parameters=[bp],
)
output = UnityRLOutputProto(agentInfos=self._get_agent_infos())
return UnityOutputProto(rl_initialization_output=rl_init, rl_output=output)
def test_agent_group_spec_from_proto():
agent_proto = generate_list_agent_proto(1, [(3, ), (4, )])[0]
bp = BrainParametersProto()
bp.vector_action_size.extend([5, 4])
bp.vector_action_space_type = 0
group_spec = agent_group_spec_from_proto(bp, agent_proto)
assert group_spec.is_action_discrete()
assert not group_spec.is_action_continuous()
assert group_spec.observation_shapes == [(3, ), (4, )]
assert group_spec.discrete_action_branches == (5, 4)
assert group_spec.action_size == 2
bp = BrainParametersProto()
bp.vector_action_size.extend([6])
bp.vector_action_space_type = 1
group_spec = agent_group_spec_from_proto(bp, agent_proto)
assert not group_spec.is_action_discrete()
assert group_spec.is_action_continuous()
assert group_spec.action_size == 6
def load_demonstration(file_path: str) -> Tuple[BrainParameters, List[BrainInfo], int]:
"""
Loads and parses a demonstration file.
:param file_path: Location of demonstration file (.demo).
:return: BrainParameter and list of BrainInfos containing demonstration data.
"""
# First 32 bytes of file dedicated to meta-data.
INITIAL_POS = 33
file_paths = []
if os.path.isdir(file_path):
all_files = os.listdir(file_path)
for _file in all_files:
if _file.endswith(".demo"):
file_paths.append(os.path.join(file_path, _file))
if not all_files:
raise ValueError("There are no '.demo' files in the provided directory.")
elif os.path.isfile(file_path):
file_paths.append(file_path)
file_extension = pathlib.Path(file_path).suffix
if file_extension != ".demo":
raise ValueError(
"The file is not a '.demo' file. Please provide a file with the "
"correct extension."
)
else:
raise FileNotFoundError(
"The demonstration file or directory {} does not exist.".format(file_path)
)
brain_params = None
brain_param_proto = None
brain_infos = []
total_expected = 0
for _file_path in file_paths:
data = open(_file_path, "rb").read()
next_pos, pos, obs_decoded = 0, 0, 0
while pos < len(data):
next_pos, pos = _DecodeVarint32(data, pos)
if obs_decoded == 0:
meta_data_proto = DemonstrationMetaProto()
meta_data_proto.ParseFromString(data[pos : pos + next_pos])
total_expected += meta_data_proto.number_steps
pos = INITIAL_POS
if obs_decoded == 1:
brain_param_proto = BrainParametersProto()
brain_param_proto.ParseFromString(data[pos : pos + next_pos])
pos += next_pos
if obs_decoded > 1:
agent_info = AgentInfoProto()
agent_info.ParseFromString(data[pos : pos + next_pos])
if brain_params is None:
brain_params = BrainParameters.from_proto(
brain_param_proto, agent_info
)
brain_info = BrainInfo.from_agent_proto(0, [agent_info], brain_params)
brain_infos.append(brain_info)
if len(brain_infos) == total_expected:
break
pos += next_pos
obs_decoded += 1
return brain_params, brain_infos, total_expected