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a new type of env that does not perform line disconection on a masked…
… part of the grid, see rte-france#571
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| Original file line number | Diff line number | Diff line change |
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| # Copyright (c) 2023, 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 copy | ||
| import numpy as np | ||
| from typing import Tuple, Union, List | ||
| from grid2op.Environment.environment import Environment | ||
| from grid2op.Action import BaseAction | ||
| from grid2op.Observation import BaseObservation | ||
| from grid2op.Exceptions import EnvError | ||
| from grid2op.dtypes import dt_bool, dt_float, dt_int | ||
|
|
||
|
|
||
| class MaskedEnvironment(Environment): # TODO heritage ou alors on met un truc de base | ||
| """This class is the grid2op implementation of a "maked" environment: lines not in the | ||
| `lines_of_interest` mask will NOT be deactivated by the environment is the flow is too high | ||
| (or moderately high for too long.) | ||
| .. warning:: | ||
| This class might not behave normally if used with TimeOutEnvironment, MultiEnv, MultiMixEnv etc. | ||
| .. warning:: | ||
| At time of writing, the behaviour of "obs.simulate" is not modified | ||
| """ | ||
| CAN_SKIP_TS = False # some steps can be more than one time steps | ||
| def __init__(self, | ||
| grid2op_env: Union[Environment, dict], | ||
| lines_of_interest): | ||
|
|
||
| self._lines_of_interest = self._make_lines_of_interest(lines_of_interest) | ||
| if isinstance(grid2op_env, Environment): | ||
| super().__init__(**grid2op_env.get_kwargs()) | ||
| elif isinstance(grid2op_env, dict): | ||
| super().__init__(**grid2op_env) | ||
| else: | ||
| raise EnvError(f"For TimedOutEnvironment you need to provide " | ||
| f"either an Environment or a dict " | ||
| f"for grid2op_env. You provided: {type(grid2op_env)}") | ||
|
|
||
| def _make_lines_of_interest(self, lines_of_interest): | ||
| # NB is called BEFORE the env has been created... | ||
| if isinstance(lines_of_interest, np.ndarray): | ||
| # if lines_of_interest.size() != type(self).n_line: | ||
| # raise EnvError("Impossible to init A masked environment when the number of lines " | ||
| # "of the mask do not match the number of lines on the grid.") | ||
| res = lines_of_interest.astype(dt_bool) | ||
| if res.sum() == 0: | ||
| raise EnvError("You cannot use MaskedEnvironment and masking all " | ||
| "the grid. If you don't want to simulate powerline " | ||
| "disconnection when they are game over, please " | ||
| "set params.NO_OVERFLOW_DISCONNECT=True (see doc)") | ||
| else: | ||
| raise EnvError("Format of lines_of_interest is not understood. " | ||
| "Please provide a vector of the size of the " | ||
| "number of lines on the grid.") | ||
| return res | ||
|
|
||
| def _reset_vectors_and_timings(self): | ||
| super()._reset_vectors_and_timings() | ||
| self._hard_overflow_threshold[~self._lines_of_interest] = 1e-7 * np.finfo(dt_float).max # some kind of infinity value | ||
| # NB we multiply np.finfo(dt_float).max by a small number to avoid overflow | ||
| # indeed, _hard_overflow_threshold is multiply by the flow on the lines | ||
| self._nb_timestep_overflow_allowed[~self._lines_of_interest] = np.iinfo(dt_int).max - 1 # some kind of infinity value | ||
|
|
||
| def get_kwargs(self, with_backend=True, with_chronics_handler=True): | ||
| res = {} | ||
| res["lines_of_interest"] = copy.deepcopy(self._lines_of_interest) | ||
| res["grid2op_env"] = super().get_kwargs(with_backend, with_chronics_handler) | ||
| return res | ||
|
|
||
| def get_params_for_runner(self): | ||
| res = super().get_params_for_runner() | ||
| res["envClass"] = MaskedEnvironment | ||
| res["other_env_kwargs"] = {"lines_of_interest": copy.deepcopy(self._lines_of_interest)} | ||
| return res | ||
|
|
||
| @classmethod | ||
| def init_obj_from_kwargs(cls, | ||
| other_env_kwargs, | ||
| init_env_path, | ||
| init_grid_path, | ||
| chronics_handler, | ||
| backend, | ||
| parameters, | ||
| name, | ||
| names_chronics_to_backend, | ||
| actionClass, | ||
| observationClass, | ||
| rewardClass, | ||
| legalActClass, | ||
| voltagecontrolerClass, | ||
| other_rewards, | ||
| opponent_space_type, | ||
| opponent_action_class, | ||
| opponent_class, | ||
| opponent_init_budget, | ||
| opponent_budget_per_ts, | ||
| opponent_budget_class, | ||
| opponent_attack_duration, | ||
| opponent_attack_cooldown, | ||
| kwargs_opponent, | ||
| with_forecast, | ||
| attention_budget_cls, | ||
| kwargs_attention_budget, | ||
| has_attention_budget, | ||
| logger, | ||
| kwargs_observation, | ||
| observation_bk_class, | ||
| observation_bk_kwargs, | ||
| _raw_backend_class, | ||
| _read_from_local_dir): | ||
| res = MaskedEnvironment(grid2op_env={"init_env_path": init_env_path, | ||
| "init_grid_path": init_grid_path, | ||
| "chronics_handler": chronics_handler, | ||
| "backend": backend, | ||
| "parameters": parameters, | ||
| "name": name, | ||
| "names_chronics_to_backend": names_chronics_to_backend, | ||
| "actionClass": actionClass, | ||
| "observationClass": observationClass, | ||
| "rewardClass": rewardClass, | ||
| "legalActClass": legalActClass, | ||
| "voltagecontrolerClass": voltagecontrolerClass, | ||
| "other_rewards": other_rewards, | ||
| "opponent_space_type": opponent_space_type, | ||
| "opponent_action_class": opponent_action_class, | ||
| "opponent_class": opponent_class, | ||
| "opponent_init_budget": opponent_init_budget, | ||
| "opponent_budget_per_ts": opponent_budget_per_ts, | ||
| "opponent_budget_class": opponent_budget_class, | ||
| "opponent_attack_duration": opponent_attack_duration, | ||
| "opponent_attack_cooldown": opponent_attack_cooldown, | ||
| "kwargs_opponent": kwargs_opponent, | ||
| "with_forecast": with_forecast, | ||
| "attention_budget_cls": attention_budget_cls, | ||
| "kwargs_attention_budget": kwargs_attention_budget, | ||
| "has_attention_budget": has_attention_budget, | ||
| "logger": logger, | ||
| "kwargs_observation": kwargs_observation, | ||
| "observation_bk_class": observation_bk_class, | ||
| "observation_bk_kwargs": observation_bk_kwargs, | ||
| "_raw_backend_class": _raw_backend_class, | ||
| "_read_from_local_dir": _read_from_local_dir}, | ||
| **other_env_kwargs) | ||
| return res |
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