Source code for grid2op.Reward.LinesReconnectedReward

# 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


[docs]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 """
[docs] def __init__(self, logger=None): BaseReward.__init__(self, logger=logger) self.reward_min = dt_float(0.0) self.reward_max = dt_float(1.0) self.penalty_max_at_n_lines = dt_float(2.0)
[docs] 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)