Gym controllers

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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.

The use, dissemination or disclosure of data in this file is subject to limitation or restriction. See accompanying README and LICENSE for details.

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Provides controllers to pass actions to wrapped OpenAI Gym Environments

OpenAIGymDuplicateController (OpenAIGymMainController)

GymController implementation for passing actions to wrapped OpenAI Gym Environments

Source code in corl/simulators/openai_gym/gym_controllers.py

class OpenAIGymDuplicateController(OpenAIGymMainController):
    """
    GymController implementation for passing actions to wrapped OpenAI Gym Environments
    """

    @property
    def exclusiveness(self) -> typing.Set[str]:
        """Return exclusiveness"""
        return set()

exclusiveness: Set[str] property readonly

Return exclusiveness

OpenAIGymMainController (BaseController)

GymController implementation for passing actions to wrapped OpenAI Gym Environments

Source code in corl/simulators/openai_gym/gym_controllers.py

class OpenAIGymMainController(BaseController):
    """
    GymController implementation for passing actions to wrapped OpenAI Gym Environments
    """

    def __init__(self, parent_platform, config=None):

        act_space = parent_platform.action_space
        cont_prop: typing.Union[DiscreteProp, BoxProp]

        if isinstance(act_space, gym.spaces.Discrete):

            class DiscreteGymProp(DiscreteProp):
                """
                DiscreteGymProp can be updated via config and valdidate by pydantic
                """
                name: str = "gym controller"
                unit: str = "None"
                n: int = act_space.n
                description: str = "gym env action space"

            cont_prop = DiscreteGymProp

        elif isinstance(act_space, gym.spaces.Box):

            class BoxGymProp(BoxProp):
                """
                BoxGymProp can be updated via config and valdidate by pydantic
                """
                name: str = "gym controller"
                low: typing.List[float] = act_space.low.tolist()
                high: typing.List[float] = act_space.high.tolist()
                dtype: np.dtype = act_space.dtype
                unit: typing.List[str] = ["None"] * len(act_space.low)
                description: str = "gym env action space"

            cont_prop = BoxGymProp
        else:
            raise RuntimeError(f"This controller does not currently know how to handle a {type(act_space)} action space")

        super().__init__(parent_platform=parent_platform, config=config, property_class=cont_prop)

    @property
    def exclusiveness(self) -> typing.Set[str]:
        """Return exclusiveness"""
        return {"main_controller"}

    def apply_control(self, control: np.ndarray) -> None:
        self.parent_platform.save_action_to_platform(control)

    def get_applied_control(self) -> typing.Union[np.ndarray, numbers.Number]:
        return self.parent_platform.get_applied_action()

exclusiveness: Set[str] property readonly

Return exclusiveness

apply_control(self, control)

The generic method to apply the control for this controller.

Parameters

control The control to be executed by the controller

Source code in corl/simulators/openai_gym/gym_controllers.py

def apply_control(self, control: np.ndarray) -> None:
    self.parent_platform.save_action_to_platform(control)

get_applied_control(self)

Get the previously applied control that was given to the apply_control function Returns

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previously applied control that was given to the apply_control function

Source code in corl/simulators/openai_gym/gym_controllers.py

def get_applied_control(self) -> typing.Union[np.ndarray, numbers.Number]:
    return self.parent_platform.get_applied_action()