# 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 typing import List
import numpy as np
from grid2op.dtypes import dt_bool
from grid2op.Observation import BaseObservation
from grid2op.Action import BaseAction, ActionSpace
from grid2op.Agent.greedyAgent import GreedyAgent
[docs]class PowerLineSwitch(GreedyAgent):
"""
This is a :class:`GreedyAgent` example, which will attempt to disconnect powerlines.
It will choose among:
- doing nothing
- changing the status of one powerline
which action that will maximize the simulated reward. All powerlines are tested at each steps. This means
that if `n` is the number of powerline on the grid, at each steps this actions will perform `n` +1
calls to "simulate" (one to do nothing and one that change the status of each powerline)
"""
def __init__(self, action_space: ActionSpace, simulated_time_step : int =1):
GreedyAgent.__init__(self, action_space, simulated_time_step=simulated_time_step)
[docs] def _get_tested_action(self, observation: BaseObservation) -> List[BaseAction]:
res = [self.action_space({})] # add the do nothing
for i in range(self.action_space.n_line):
tmp = np.full(self.action_space.n_line, fill_value=False, dtype=dt_bool)
tmp[i] = True
action = self.action_space({"change_line_status": tmp})
res.append(action)
return res