# Copyright (c) 2019-2020, RTE (https://www.rte-france.com)
# 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 http://mozilla.org/MPL/2.0/.
# SPDX-License-Identifier: MPL-2.0
# This file is part of Grid2Op, Grid2Op a testbed platform to model sequential decision making in power systems.
from grid2op.Reward.baseReward import BaseReward
from grid2op.dtypes import dt_float
[docs]class ConstantReward(BaseReward):
"""
Most basic implementation of reward: everything has the same values: 0.0
Note that this :class:`BaseReward` subtype is not useful at all, whether to train an :attr:`BaseAgent`
nor to assess its performance of course.
Examples
---------
You can use this reward in any environment with:
.. code-block:: python
import grid2op
from grid2op.Reward import ConstantReward
# then you create your environment with it:
NAME_OF_THE_ENVIRONMENT = "l2rpn_case14_sandbox"
env = grid2op.make(NAME_OF_THE_ENVIRONMENT,reward_class=ConstantReward)
# and do a step with a "do nothing" action
obs = env.reset()
obs, reward, done, info = env.step(env.action_space())
# the reward is 0., always... Not really useful
"""
[docs] def __init__(self, logger=None):
BaseReward.__init__(self, logger=logger)
[docs] def __call__(self, action, env, has_error, is_done, is_illegal, is_ambiguous):
return dt_float(0.0)