Source code for grid2op.Reward.CombinedScaledReward

# Copyright (c) 2019-2020, RTE (
# See AUTHORS.txt
# This Source Code Form is subject to the terms of the Mozilla Public License, version 2.0.
# If a copy of the Mozilla Public License, version 2.0 was not distributed with this file,
# you can obtain one at
# SPDX-License-Identifier: MPL-2.0
# This file is part of Grid2Op, Grid2Op a testbed platform to model sequential decision making in power systems.

import numpy as np

from grid2op.Reward.CombinedReward import CombinedReward
from grid2op.dtypes import dt_float

[docs]class CombinedScaledReward(CombinedReward): """ This class allows to combine multiple rewards. It will compute a scaled reward of the weighted sum of the registered rewards. Scaling is done by linearly interpolating the weighted sum, from the range [min_sum; max_sum] to [reward_min; reward_max] min_sum and max_sum are computed from the weights and ranges of registered rewards. See :class:`Reward.BaseReward` for setting the output range. Examples -------- .. code-block:: python import grid2op from grid2op.Reward import GameplayReward, FlatReward, CombinedScaledReward env = grid2op.make(..., reward_class=CombinedScaledReward) cr = self.env.get_reward_instance() cr.addReward("Gameplay", GameplayReward(), 1.0) cr.addReward("Flat", FlatReward(), 1.0) cr.initialize(self.env) obs = env.reset() obs, reward, done, info = env.step(env.action_space()) # reward here is computed by summing the results of what would have # given `GameplayReward` and the one from `FlatReward` """
[docs] def __init__(self, logger=None): super().__init__(logger=logger) self.reward_min = dt_float(-0.5) self.reward_max = dt_float(0.5) self._sum_max = dt_float(0.0) self._sum_min = dt_float(0.0) self.rewards = {}
[docs] def initialize(self, env): """ Overloaded initialze from `Reward.CombinedReward`. This is because it needs to store the ranges internaly """ self._sum_max = dt_float(0.0) self._sum_min = dt_float(0.0) for key, reward in self.rewards.items(): reward_w = dt_float(reward["weight"]) reward_instance = reward["instance"] reward_instance.initialize(env) self._sum_max += dt_float(reward_instance.reward_max * reward_w) self._sum_min += dt_float(reward_instance.reward_min * reward_w)
[docs] def __call__(self, action, env, has_error, is_done, is_illegal, is_ambiguous): # Get weighted sum from parent ws = super().__call__(action, env, has_error, is_done, is_illegal, is_ambiguous) # Scale to range res = np.interp( ws, [self._sum_min, self._sum_max], [self.reward_min, self.reward_max] ) return dt_float(res)
[docs] def close(self): super().close()