#
# Copyright (c) 2017 Intel Corporation
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# 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
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# 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.
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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
[docs]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