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Env func base

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.

Functors are objects the that can be treated as though they are a function. When to use functors? - Functors are used when you want to hide/abstract the real implementation. Let’s say you want to call the different functions depending on the input but you don’t want the user code to make explicit calls to those different functions. This is the ideal situation where functors can help. - In this scenario, we can go for a functor which internally calls the most suitable function depending on the input. - Now if later, none of functions to be called increases, then it would be just a simple change in the backend code without disturbing any of the user code. Thus functors help in creating maintainable, decoupled and extendable codes.

EnvFuncBase ¤

base definition for env functions

Source code in corl/libraries/env_func_base.py 
class EnvFuncBase:
    """ base definition for env functions
    """

    def reset(self):  # pylint: disable=no-self-use
        """ Base reset function for items such as rewards and dones
        """
        ...

    @property
    def name(self) -> str:
        """ gets the name fo the functor

        Returns
        -------
        str
            The name of the functor
        """
        return type(self).__name__

name: str property readonly ¤

gets the name fo the functor

Returns¤

str The name of the functor

reset(self) ¤

Base reset function for items such as rewards and dones

Source code in corl/libraries/env_func_base.py 
def reset(self):  # pylint: disable=no-self-use
    """ Base reset function for items such as rewards and dones
    """
    ...