# 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.Agent.greedyAgent import GreedyAgent
[docs]class RecoPowerlineAgent(GreedyAgent):
"""
This is a :class:`GreedyAgent` example, which will attempt to reconnect powerlines: for each disconnected powerline
that can be reconnected, it will simulate the effect of reconnecting it. And reconnect the one that lead to the
highest simulated reward.
"""
def __init__(self, action_space):
GreedyAgent.__init__(self, action_space)
[docs] def _get_tested_action(self, observation):
res = [self.action_space({})] # add the do nothing
line_stat_s = observation.line_status
cooldown = observation.time_before_cooldown_line
can_be_reco = ~line_stat_s & (cooldown == 0)
if can_be_reco.any():
res = [
self.action_space({"set_line_status": [(id_, +1)]})
for id_ in np.nonzero(can_be_reco)[0]
]
return res