Source code for grid2op.Reward.DistanceReward

# 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 DistanceReward(BaseReward): """ This reward computes a penalty based on the distance of the current grid to the grid at time 0 where everything is connected to bus 1. Examples --------- You can use this reward in any environment with: .. code-block: import grid2op from grid2op.Reward import DistanceReward # then you create your environment with it: NAME_OF_THE_ENVIRONMENT = "rte_case14_realistic" env = grid2op.make(NAME_OF_THE_ENVIRONMENT,reward_class=DistanceReward) # 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 DistanceReward 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)
[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 topo from env obs = env.get_obs() topo = obs.topo_vect idx = 0 diff = dt_float(0.0) for n_elems_on_sub in obs.sub_info: # Find this substation elements range in topology vect sub_start = idx sub_end = idx + n_elems_on_sub current_sub_topo = topo[sub_start:sub_end] # Count number of elements not on bus 1 # Because at the initial state, all elements are on bus 1 diff += dt_float(1.0) * np.count_nonzero(current_sub_topo != 1) # Set index to next sub station idx += n_elems_on_sub r = np.interp( diff, [dt_float(0.0), len(topo) * dt_float(1.0)], [self.reward_max, self.reward_min], ) return r