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

# Source code for rl_coach.filters.action.linear_box_to_box_map

    #
    # 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 LinearBoxToBoxMap(ActionFilter):
        """
        A linear mapping of two box action spaces. For example, if the action space of the
        environment consists of continuous actions between 0 and 1, and we want the agent to choose actions between -1 and 1,
        the LinearBoxToBoxMap can be used to map the range -1 and 1 to the range 0 and 1 in a linear way. This means that the
        action -1 will be mapped to 0, the action 1 will be mapped to 1, and the rest of the actions will be linearly mapped
        between those values.
        """
        def __init__(self,
                     input_space_low: Union[None, int, float, np.ndarray],
                     input_space_high: Union[None, int, float, np.ndarray]):
            """
            :param input_space_low: the low values of the desired action space
            :param input_space_high: the high values of the desired action space
            """
            self.input_space_low = input_space_low
            self.input_space_high = input_space_high
            self.rescale = None
            self.offset = None
            super().__init__()

        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))

        def get_unfiltered_action_space(self, output_action_space: BoxActionSpace) -> BoxActionSpace:
            self.input_action_space = BoxActionSpace(output_action_space.shape, self.input_space_low, self.input_space_high)
            self.rescale = \
                (output_action_space.high - output_action_space.low) / (self.input_space_high - self.input_space_low)
            self.offset = output_action_space.low - self.input_space_low
            self.output_action_space = output_action_space
            return self.input_action_space

        def filter(self, action: ActionType) -> ActionType:
            return self.output_action_space.low + (action - self.input_space_low) * self.rescale