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LinesReconnectedReward.py
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LinesReconnectedReward.py
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# 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.
import numpy as np
from grid2op.Reward.BaseReward import BaseReward
from grid2op.dtypes import dt_float
class LinesReconnectedReward(BaseReward):
"""
This reward computes a penalty
based on the number of powerline that could have been reconnected (cooldown at 0.) but
are still disconnected.
Examples
---------
You can use this reward in any environment with:
.. code-block:
import grid2op
from grid2op.Reward import LinesReconnectedReward
# then you create your environment with it:
NAME_OF_THE_ENVIRONMENT = "rte_case14_realistic"
env = grid2op.make(NAME_OF_THE_ENVIRONMENT,reward_class=LinesReconnectedReward)
# and do a step with a "do nothing" action
obs = env.reset()
obs, reward, done, info = env.step(env.action_space())
# the reward is computed with the LinesReconnectedReward class
"""
def __init__(self):
BaseReward.__init__(self)
self.reward_min = dt_float(0.0)
self.reward_max = dt_float(1.0)
self.penalty_max_at_n_lines = dt_float(2.0)
def __call__(self, action, env, has_error,
is_done, is_illegal, is_ambiguous):
if has_error or is_illegal or is_ambiguous:
return self.reward_min
# Get obs from env
obs = env.get_obs()
# All lines ids
lines_id = np.arange(env.n_line)
lines_id = lines_id[obs.time_before_cooldown_line == 0]
n_penalties = dt_float(0.0)
for line_id in lines_id:
# Line could be reconnected but isn't
if obs.line_status[line_id] == False:
n_penalties += dt_float(1.0)
max_p = self.penalty_max_at_n_lines
n_penalties = np.clip(n_penalties, dt_float(0.0), max_p)
r = np.interp(n_penalties,
[dt_float(0.0), max_p],
[self.reward_max, self.reward_min])
return dt_float(r)