Source code for grid2op.Reward.l2RPNReward

# 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.Reward.baseReward import BaseReward
from grid2op.dtypes import dt_float

[docs]class L2RPNReward(BaseReward): """ This is the historical :class:`BaseReward` used for the Learning To Run a Power Network competition on WCCI 2019 See `L2RPN <>`_ for more information. This rewards makes the sum of the "squared margin" on each powerline. The margin is defined, for each powerline as: `margin of a powerline = (thermal limit - flow in amps) / thermal limit` (if flow in amps <= thermal limit) else `margin of a powerline = 0.` This rewards is then: `sum (margin of this powerline) ^ 2`, for each powerline. Examples --------- You can use this reward in any environment with: .. code-block:: python import grid2op from grid2op.Reward import L2RPNReward # then you create your environment with it: NAME_OF_THE_ENVIRONMENT = "l2rpn_case14_sandbox" env = grid2op.make(NAME_OF_THE_ENVIRONMENT,reward_class=L2RPNReward) # 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 L2RPNReward class """
[docs] def __init__(self, logger=None): BaseReward.__init__(self, logger=logger)
[docs] def initialize(self, env): self.reward_min = dt_float(0.0) self.reward_max = dt_float(env.backend.n_line)
[docs] def __call__(self, action, env, has_error, is_done, is_illegal, is_ambiguous): if not is_done and not has_error: line_cap = self.__get_lines_capacity_usage(env) res = line_cap.sum() else: # no more data to consider, no powerflow has been run, reward is what it is res = self.reward_min # print(f"\t env.backend.get_line_flow(): {env.backend.get_line_flow()}") return res
@staticmethod def __get_lines_capacity_usage(env): ampere_flows = np.abs(env.backend.get_line_flow(), dtype=dt_float) thermal_limits = np.abs(env.get_thermal_limit(), dtype=dt_float) thermal_limits += 1e-1 # for numerical stability relative_flow = np.divide(ampere_flows, thermal_limits, dtype=dt_float) x = np.minimum(relative_flow, dt_float(1.0)) lines_capacity_usage_score = np.maximum( dt_float(1.0) - x**2, np.zeros(x.shape, dtype=dt_float) ) return lines_capacity_usage_score