# 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 time
from math import floor
from typing import Any, Dict, Tuple, Union, List, Literal
from grid2op.Environment.environment import Environment
from grid2op.Action import BaseAction
from grid2op.Observation import BaseObservation
from grid2op.Exceptions import EnvError
from grid2op.Space import DEFAULT_N_BUSBAR_PER_SUB
[docs]class TimedOutEnvironment(Environment): # TODO heritage ou alors on met un truc de base
"""This class is the grid2op implementation of a "timed out environment" entity in the RL framework.
This class is very similar to the
standard environment. They only differ in the behaivour
of the `step` function.
For more information, see the documentation of
:func:`TimedOutEnvironment.step`
.. warning::
This class might not behave normally if used with MaskedEnvironment, MultiEnv, MultiMixEnv etc.
Attributes
----------
name: ``str``
The name of the environment
time_out_ms: ``int``
maximum duration before performing a do_nothing action and updating to the next time_step.
action_space: :class:`grid2op.Action.ActionSpace`
Another name for :attr:`Environment.helper_action_player` for gym compatibility.
observation_space: :class:`grid2op.Observation.ObservationSpace`
Another name for :attr:`Environment.helper_observation` for gym compatibility.
reward_range: ``(float, float)``
The range of the reward function
metadata: ``dict``
For gym compatibility, do not use
spec: ``None``
For Gym compatibility, do not use
_viewer: ``object``
Used to display the powergrid. Currently properly supported.
"""
CAN_SKIP_TS = True # some steps can be more than one time steps
def __init__(self,
grid2op_env: Union[Environment, dict],
time_out_ms: int=1e3) -> None:
if time_out_ms <= 0.:
raise EnvError(f"For TimedOutEnvironment you need to provide "
f"a time_out_ms > 0 (currently {time_out_ms})")
self.time_out_ms = float(time_out_ms) # in ms
self.__last_act_send = time.perf_counter()
self.__last_act_received = self.__last_act_send
self._nb_dn_last = 0
self._is_init_dn = False
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)}")
self._is_init_dn = True
self._res_skipped = []
self._opp_attacks = []
[docs] def step(self, action: BaseAction) -> Tuple[BaseObservation, float, bool, dict]:
"""This function allows to pass to the
next step for the action.
Provided the action the agent wants to do, it will
perform the action on the grid and resturn the typical
"observation, reward, done, info" tuple.
Compared to :func:`BaseEnvironment.step` this function
will emulate the "time that passes" supposing that the duration
between each step should be `time_out_ms`. Indeed, in reality,
there is only 5 mins to take an action between two grid states
separated from 5 mins.
More precisely:
If your agent takes less than `time_out_ms` to chose its action
then this function behaves normally.
If your agent takes between `time_out_ms` and `2 x time_out_ms`
to provide an action then
a "do nothing" action is performed and then the provided
action is performed.
If your agent takes between `2 x time_out_ms` and `3 x time_out_ms`
to provide an action, then 2 "do nothing" actions are
performed before your action.
.. note::
It is possible that the environment "fails" before
the action of the agent is implemented on the grid.
Parameters
----------
action : `grid2op.Action.BaseAction`
The action the agent wish to perform.
Returns
-------
Tuple[BaseObservation, float, bool, dict]
_description_
"""
self.__last_act_received = time.perf_counter()
self._res_skipped = []
self._opp_attacks = []
# do the "do nothing" actions
self._nb_dn_last = 0
if self._is_init_dn:
nb_dn = floor(1000. * (self.__last_act_received - self.__last_act_send) / (self.time_out_ms))
else:
nb_dn = 0
do_nothing_action = self.action_space()
for _ in range(nb_dn):
obs, reward, done, info = super().step(do_nothing_action)
self._nb_dn_last += 1
self._opp_attacks.append(self._oppSpace.last_attack)
if done:
info["nb_do_nothing"] = nb_dn
info["nb_do_nothing_made"] = self._nb_dn_last
info["action_performed"] = False
info["last_act_received"] = self.__last_act_received
info["last_act_send"] = self.__last_act_send
return obs, reward, done, info
self._res_skipped.append((obs, reward, done, info))
# now do the action
obs, reward, done, info = super().step(action)
self._opp_attacks.append(self._oppSpace.last_attack)
info["nb_do_nothing"] = nb_dn
info["nb_do_nothing_made"] = self._nb_dn_last
info["action_performed"] = True
info["last_act_received"] = self.__last_act_received
info["last_act_send"] = self.__last_act_send
self.__last_act_send = time.perf_counter()
return obs, reward, done, info
def steps(self, action) -> Tuple[List[Tuple[BaseObservation, float, bool, dict]],
List[BaseAction]]:
tmp = self.step(action)
res = []
for el in self._res_skipped:
res.append(el)
res.append(tmp)
return res, self._opp_attacks
[docs] def get_kwargs(self, with_backend=True, with_chronics_handler=True):
res = {}
res["time_out_ms"] = self.time_out_ms
res["grid2op_env"] = super().get_kwargs(with_backend, with_chronics_handler)
return res
[docs] def get_params_for_runner(self):
res = super().get_params_for_runner()
res["envClass"] = TimedOutEnvironment
res["other_env_kwargs"] = {"time_out_ms": self.time_out_ms}
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,
n_busbar=DEFAULT_N_BUSBAR_PER_SUB):
res = TimedOutEnvironment(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,
"n_busbar": int(n_busbar)},
**other_env_kwargs)
return res
[docs] def reset(self,
*,
seed: Union[int, None] = None,
options: Union[Dict[Union[str, Literal["time serie id"]], Union[int, str]], None] = None) -> BaseObservation:
"""Reset the environment.
.. seealso::
The doc of :func:`Environment.reset` for more information
Returns
-------
BaseObservation
The first observation of the new episode.
"""
self.__last_act_send = time.perf_counter()
self.__last_act_received = self.__last_act_send
self._is_init_dn = False
res = super().reset(seed=seed, options=options)
self.__last_act_send = time.perf_counter()
self._is_init_dn = True
return res
def _custom_deepcopy_for_copy(self, new_obj):
super()._custom_deepcopy_for_copy(new_obj)
new_obj.__last_act_send = time.perf_counter()
new_obj.__last_act_received = new_obj.__last_act_send
new_obj._is_init_dn = self._is_init_dn
new_obj.time_out_ms = self.time_out_ms