Source code for grid2op.Agent.powerlineSwitch

# 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.dtypes import dt_bool
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): GreedyAgent.__init__(self, action_space)
[docs] def _get_tested_action(self, observation): 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}) if not observation.line_status[i]: # so the action consisted in reconnecting the powerline # i need to say on which bus (always on bus 1 for this type of agent) action = action.update( {"set_bus": {"lines_or_id": [(i, 1)], "lines_ex_id": [(i, 1)]}} ) res.append(action) return res