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Env creation

Air Force Research Laboratory (AFRL) Autonomous Capabilities Team (ACT3) Reinforcement Learning (RL) Core.

This is a US Government Work not subject to copyright protection in the US.

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create_agent_sim_configs(agents, agent_platforms, sim_class, avail_platforms, epp_registry, *, multiple_workers=False) ¤

Creates dictionary of agent configs used byt the simmulator

Parameters¤

agents: dictionary of agent names and configs sim_class: simulator class used by environment avail_platforms: available platforms in the simulator

Returns¤

None

Source code in corl/environment/utils/env_creation.py 
def create_agent_sim_configs(
    agents: typing.Dict[str, AgentParseInfo],
    agent_platforms: typing.Dict[str, PlatformParseInfo],
    sim_class: typing.Callable,
    avail_platforms: typing.Callable,
    epp_registry: typing.Dict[str, EpisodeParameterProvider],
    *,
    multiple_workers: bool = False
) -> typing.Tuple[typing.Dict[str, BaseAgent], dict]:
    """
    Creates  dictionary of agent configs used byt the simmulator

    Parameters
    ----------
    agents: dictionary of agent names and configs
    sim_class: simulator class used by environment
    avail_platforms: available platforms in the simulator

    Returns
    -------
    None
    """
    sim_agent_configs = {}
    agent_dict = {}

    for platform_name, platform_config in agent_platforms.items():
        agent_platform = avail_platforms.ParseFromNameModel(platform_config)  # type: ignore

        agent_part_cls: typing.List = []
        agent_part_cfg: typing.List = []
        for agent_name, agent_configs in agents.items():
            if agent_configs.platform_name != platform_name:
                continue

            agent_class = agent_configs.class_config.agent(
                **agent_configs.class_config.config,
                epp=epp_registry[agent_name],
                agent_name=agent_name,
                platform_name=platform_name,
                multiple_workers=multiple_workers
            )
            agent_dict[agent_name] = agent_class
            partial_agent_part_list = agent_class.get_platform_parts(sim_class, agent_platform)

            for (cls, cfg) in partial_agent_part_list:
                unique = True
                for i, part_cls in enumerate(agent_part_cls):
                    if cls == part_cls and agent_part_cfg[i] == cfg:
                        unique = False
                        break

                if unique:
                    agent_part_cls.append(cls)
                    agent_part_cfg.append(cfg)

        agent_part_list = list(zip(agent_part_cls, agent_part_cfg))

        sim_agent_configs[platform_name] = {
            "platform_config": platform_config,
            "parts_list": agent_part_list,
        }

    return agent_dict, sim_agent_configs