# rl_coach.filters.observation.observation_clipping_filter — Reinforcement Learning Coach 0.12.0 documentation

# Source code for rl_coach.filters.observation.observation_clipping_filter

    #
    # 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.
    #

    import numpy as np

    from rl_coach.core_types import ObservationType
    from rl_coach.filters.observation.observation_filter import ObservationFilter
    from rl_coach.spaces import ObservationSpace

    class ObservationClippingFilter(ObservationFilter):
        """
        Clips the observation values to a given range of values.
        For example, if the observation consists of measurements in an arbitrary range,
        and we want to control the minimum and maximum values of these observations,
        we can define a range and clip the values of the measurements.
        """
        def __init__(self, clipping_low: float=-np.inf, clipping_high: float=np.inf):
            """
            :param clipping_low: The minimum value to allow after normalizing the observation
            :param clipping_high: The maximum value to allow after normalizing the observation
            """
            super().__init__()
            self.clip_min = clipping_low
            self.clip_max = clipping_high

        def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
            observation = np.clip(observation, self.clip_min, self.clip_max)

            return observation

        def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
            return input_observation_space