# rl_coach.filters.action.box_masking — Reinforcement Learning Coach 0.12.0 documentation

# Source code for rl_coach.filters.action.box_masking

    #
    # Copyright (c) 2017 Intel Corporation
    #
    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    #      http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.
    #

    from typing import Union

    import numpy as np

    from rl_coach.core_types import ActionType
    from rl_coach.filters.action.action_filter import ActionFilter
    from rl_coach.spaces import BoxActionSpace

    class BoxMasking(ActionFilter):
        """
        Masks part of the action space to enforce the agent to work in a defined space. For example,
        if the original action space is between -1 and 1, then this filter can be used in order to constrain the agent actions
        to the range 0 and 1 instead. This essentially masks the range -1 and 0 from the agent.
        The resulting action space will be shifted and will always start from 0 and have the size of the unmasked area.
        """
        def __init__(self,
                     masked_target_space_low: Union[None, int, float, np.ndarray],
                     masked_target_space_high: Union[None, int, float, np.ndarray]):
            """
            :param masked_target_space_low: the lowest values that can be chosen in the target action space
            :param masked_target_space_high: the highest values that can be chosen in the target action space
            """
            self.masked_target_space_low = masked_target_space_low
            self.masked_target_space_high = masked_target_space_high
            self.offset = masked_target_space_low
            super().__init__()

        def set_masking(self, masked_target_space_low: Union[None, int, float, np.ndarray],
                        masked_target_space_high: Union[None, int, float, np.ndarray]):
            self.masked_target_space_low = masked_target_space_low
            self.masked_target_space_high = masked_target_space_high
            self.offset = masked_target_space_low
            if self.output_action_space:
                self.validate_output_action_space(self.output_action_space)
                self.input_action_space = BoxActionSpace(self.output_action_space.shape,
                                                         low=0,
                                                         high=self.masked_target_space_high - self.masked_target_space_low)

        def validate_output_action_space(self, output_action_space: BoxActionSpace):
            if not isinstance(output_action_space, BoxActionSpace):
                raise ValueError("BoxActionSpace discretization only works with an output space of type BoxActionSpace. "
                                 "The given output space is {}".format(output_action_space))
            if self.masked_target_space_low is None or self.masked_target_space_high is None:
                raise ValueError("The masking target space size was not set. Please call set_masking.")
            if not (np.all(output_action_space.low <= self.masked_target_space_low)
                    and np.all(self.masked_target_space_low <= output_action_space.high)):
                raise ValueError("The low values for masking the action space ({}) are not within the range of the "
                                 "target space (low = {}, high = {})"
                                 .format(self.masked_target_space_low, output_action_space.low, output_action_space.high))
            if not (np.all(output_action_space.low <= self.masked_target_space_high)
                    and np.all(self.masked_target_space_high <= output_action_space.high)):
                raise ValueError("The high values for masking the action space ({}) are not within the range of the "
                                 "target space (low = {}, high = {})"
                                 .format(self.masked_target_space_high, output_action_space.low, output_action_space.high))

        def get_unfiltered_action_space(self, output_action_space: BoxActionSpace) -> BoxActionSpace:
            self.output_action_space = output_action_space
            self.input_action_space = BoxActionSpace(output_action_space.shape,
                                                     low=0,
                                                     high=self.masked_target_space_high - self.masked_target_space_low)
            return self.input_action_space

        def filter(self, action: ActionType) -> ActionType:
            return action + self.offset