Source code for grid2op.Reward.EpisodeDurationReward

# 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 EpisodeDurationReward(BaseReward): """ This reward will always be 0., unless at the end of an episode where it will return the number of steps made by the agent divided by the total number of steps possible in the episode. Examples --------- You can use this reward in any environment with: .. code-block: import grid2op from grid2op.Reward import EpisodeDurationReward # then you create your environment with it: NAME_OF_THE_ENVIRONMENT = "rte_case14_realistic" env = grid2op.make(NAME_OF_THE_ENVIRONMENT,reward_class=EpisodeDurationReward) # 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 EpisodeDurationReward class Notes ----- In case of an environment being "fast forward" (see :func:`grid2op.Environment.BaseEnv.fast_forward_chronics`) the time "during" the fast forward are counted "as if" they were successful. This means that if you "fast forward" up until the end of an episode, you are likely to receive a reward of 1.0 """
[docs] def __init__(self, per_timestep=1, logger=None): BaseReward.__init__(self, logger=logger) self.per_timestep = dt_float(per_timestep) self.total_time_steps = dt_float(0.0) self.reward_min = dt_float(0.0) self.reward_max = dt_float(1.0)
[docs] def initialize(self, env): self.reset(env)
[docs] def reset(self, env): if env.chronics_handler.max_timestep() > 0: self.total_time_steps = env.max_episode_duration() * self.per_timestep else: self.total_time_steps = np.inf self.reward_max = np.inf
[docs] def __call__(self, action, env, has_error, is_done, is_illegal, is_ambiguous): if is_done: res = env.nb_time_step if np.isfinite(self.total_time_steps): res /= self.total_time_steps else: res = self.reward_min return res