# rl_coach.filters.reward.reward_clipping_filter — Reinforcement Learning Coach 0.12.0 documentation

# Source code for rl_coach.filters.reward.reward_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 RewardType
    from rl_coach.filters.reward.reward_filter import RewardFilter
    from rl_coach.spaces import RewardSpace

    class RewardClippingFilter(RewardFilter):
        """
        Clips the reward values into a given range. For example, in DQN, the Atari rewards are
        clipped into the range -1 and 1 in order to control the scale of the returns.
        """
        def __init__(self, clipping_low: float=-np.inf, clipping_high: float=np.inf):
            """
            :param clipping_low: The low threshold for reward clipping
            :param clipping_high: The high threshold for reward clipping
            """
            super().__init__()
            self.clipping_low = clipping_low
            self.clipping_high = clipping_high

            if clipping_low > clipping_high:
                raise ValueError("The reward clipping low must be lower than the reward clipping max")

        def filter(self, reward: RewardType, update_internal_state: bool=True) -> RewardType:
            reward = float(reward)

            if self.clipping_high:
                reward = min(reward, self.clipping_high)
            if self.clipping_low:
                reward = max(reward, self.clipping_low)

            return reward

        def get_filtered_reward_space(self, input_reward_space: RewardSpace) -> RewardSpace:
            input_reward_space.high = min(self.clipping_high, input_reward_space.high)
            input_reward_space.low = max(self.clipping_low, input_reward_space.low)
            return input_reward_space