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 _update_group_specs(self, output: UnityOutputProto) -> None: init_output = output.rl_initialization_output for brain_param in init_output.brain_parameters: # Each BrainParameter in the rl_initialization_output should have at least one AgentInfo # Get that agent, because we need some of its observations. agent_infos = output.rl_output.agentInfos[brain_param.brain_name] if agent_infos.value: agent = agent_infos.value[0] new_spec = agent_group_spec_from_proto(brain_param, agent) self._env_specs[brain_param.brain_name] = new_spec logger.info(f"Connected new brain:\n{brain_param.brain_name}")
def load_demonstration( file_path: str ) -> Tuple[BrainParameters, List[AgentInfoActionPairProto], int]: """ Loads and parses a demonstration file. :param file_path: Location of demonstration file (.demo). :return: BrainParameter and list of AgentInfoActionPairProto containing demonstration data. """ # First 32 bytes of file dedicated to meta-data. INITIAL_POS = 33 file_paths = get_demo_files(file_path) group_spec = None brain_param_proto = None info_action_pairs = [] total_expected = 0 for _file_path in file_paths: with open(_file_path, "rb") as fp: with hierarchical_timer("read_file"): data = fp.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_action = AgentInfoActionPairProto() agent_info_action.ParseFromString(data[pos : pos + next_pos]) if group_spec is None: group_spec = agent_group_spec_from_proto( brain_param_proto, agent_info_action.agent_info ) info_action_pairs.append(agent_info_action) if len(info_action_pairs) == total_expected: break pos += next_pos obs_decoded += 1 if not group_spec: raise RuntimeError( f"No BrainParameters found in demonstration file at {file_path}." ) return group_spec, info_action_pairs, total_expected
def load_demonstration( file_path: str ) -> Tuple[BrainParameters, List[AgentInfoActionPairProto], int]: """ Loads and parses a demonstration file. :param file_path: Location of demonstration file (.demo). :return: BrainParameter and list of AgentInfoActionPairProto 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) ) group_spec = None brain_param_proto = None info_action_pairs = [] total_expected = 0 for _file_path in file_paths: with open(_file_path, "rb") as fp: with hierarchical_timer("read_file"): data = fp.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_action = AgentInfoActionPairProto() agent_info_action.ParseFromString(data[pos : pos + next_pos]) if group_spec is None: group_spec = agent_group_spec_from_proto( brain_param_proto, agent_info_action.agent_info ) info_action_pairs.append(agent_info_action) if len(info_action_pairs) == total_expected: break pos += next_pos obs_decoded += 1 if not group_spec: raise RuntimeError( f"No BrainParameters found in demonstration file at {file_path}." ) return group_spec, info_action_pairs, total_expected