# 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 os
import warnings
import copy
from multiprocessing import Pool
from typing import Tuple, Optional, List, Union
from grid2op.Environment import BaseEnv
from grid2op.Action import BaseAction, TopologyAction, DontAct
from grid2op.Exceptions import Grid2OpException, EnvError
from grid2op.Observation import CompleteObservation, BaseObservation
from grid2op.Opponent.opponentSpace import OpponentSpace
from grid2op.Reward import FlatReward, BaseReward
from grid2op.Rules import AlwaysLegal, BaseRules
from grid2op.Environment import Environment
from grid2op.Chronics import ChronicsHandler, GridStateFromFile, GridValue
from grid2op.Backend import Backend, PandaPowerBackend
from grid2op.Parameters import Parameters
from grid2op.Agent import DoNothingAgent, BaseAgent
from grid2op.VoltageControler import ControlVoltageFromFile
from grid2op.dtypes import dt_float
from grid2op.Opponent import BaseOpponent, NeverAttackBudget
from grid2op.operator_attention import LinearAttentionBudget
from grid2op.Runner.aux_fun import (
_aux_run_one_episode,
_aux_make_progress_bar,
_aux_one_process_parrallel,
)
from grid2op.Runner.basic_logger import DoNothingLog, ConsoleLog
from grid2op.Episode import EpisodeData
# on windows if i start using sequential, i need to continue using sequential
# if i start using parallel i need to continue using parallel
# so i force the usage of the "starmap" stuff even if there is one process on windows
from grid2op._glop_platform_info import _IS_WINDOWS, _IS_LINUX, _IS_MACOS
runner_returned_type = Union[Tuple[str, str, float, int, int],
Tuple[str, str, float, int, int, EpisodeData],
Tuple[str, str, float, int, int, EpisodeData, int]]
# TODO have a vectorized implementation of everything in case the agent is able to act on multiple environment
# at the same time. This might require a lot of work, but would be totally worth it!
# (especially for Neural Net based agents)
# TODO add a more suitable logging strategy
# TODO use gym logger if specified by the user.
# TODO: if chronics are "loop through" multiple times, only last results are saved. :-/
[docs]class Runner(object):
"""
A runner is a utility tool that allows to run simulations more easily.
It is a more convenient way to execute the
following loops:
.. code-block:: python
import grid2op
from grid2op.Agent import RandomAgent # for example...
from grid2op.Runner import Runner
env = grid2op.make("l2rpn_case14_sandbox")
###############
# the gym loops
nb_episode = 5
for i in range(nb_episode):
obs = env.reset()
done = False
reward = env.reward_range[0]
while not done:
act = agent.act(obs, reward, done)
obs, reward, done, info = env.step(act)
###############
# equivalent with use of a Runner
runner = Runner(**env.get_params_for_runner(), agentClass=RandomAgent)
res = runner.run(nb_episode=nn_episode)
This specific class as for main purpose to evaluate the performance of a trained
:class:`grid2op.Agent.BaseAgent` rather than to train it.
It has also the good property to be able to save the results of a experiment in a standardized
manner described in the :class:`grid2op.Episode.EpisodeData`.
**NB** we do not recommend to create a runner from scratch by providing all the arguments. We strongly
encourage you to use the :func:`grid2op.Environment.Environment.get_params_for_runner` for
creating a runner.
Attributes
----------
envClass: ``type``
The type of the environment used for the game. The class should be given, and **not** an instance (object) of
this class. The default is the :class:`grid2op.Environment`. If modified, it should derived from this class.
other_env_kwargs: ``dict``
Other kwargs used to build the environment (None for "nothing")
actionClass: ``type``
The type of action that can be performed by the agent / bot / controler. The class should be given, and
**not** an instance of this class. This type
should derived from :class:`grid2op.BaseAction`. The default is :class:`grid2op.TopologyAction`.
observationClass: ``type``
This type represents the class that will be used to build the :class:`grid2op.BaseObservation` visible by the
:class:`grid2op.BaseAgent`. As :attr:`Runner.actionClass`, this should be a type, and **not** and instance
(object)
of this type. This type should derived from :class:`grid2op.BaseObservation`. The default is
:class:`grid2op.CompleteObservation`.
rewardClass: ``type``
Representes the type used to build the rewards that are given to the :class:`BaseAgent`. As
:attr:`Runner.actionClass`, this should be a type, and **not** and instance (object) of this type.
This type should derived from :class:`grid2op.BaseReward`. The default is :class:`grid2op.ConstantReward` that
**should not** be used to train or evaluate an agent, but rather as debugging purpose.
gridStateclass: ``type``
This types control the mechanisms to read chronics and assign data to the powergrid. Like every "\\.*Class"
attributes the type should be pass and not an intance (object) of this type. Its default is
:class:`grid2op.GridStateFromFile` and it must be a subclass of :class:`grid2op.GridValue`.
legalActClass: ``type``
This types control the mechanisms to assess if an :class:`grid2op.BaseAction` is legal.
Like every "\\.*Class" attributes the type should be pass and not an intance (object) of this type.
Its default is :class:`grid2op.AlwaysLegal` and it must be a subclass of :class:`grid2op.BaseRules`.
backendClass: ``type``
This types control the backend, *eg.* the software that computes the powerflows.
Like every "\\.*Class" attributes the type should be pass and not an intance (object) of this type.
Its default is :class:`grid2op.PandaPowerBackend` and it must be a subclass of :class:`grid2op.Backend`.
backend_kwargs: ``dict``
Optional arguments used to build the backend. These arguments will not
be copied to create the backend used by the runner. They might
required to be pickeable on some plateform when using multi processing.
agentClass: ``type``
This types control the type of BaseAgent, *eg.* the bot / controler that will take :class:`grid2op.BaseAction`
and
avoid cascading failures.
Like every "\\.*Class" attributes the type should be pass and not an intance (object) of this type.
Its default is :class:`grid2op.DoNothingAgent` and it must be a subclass of :class:`grid2op.BaseAgent`.
logger:
A object than can be used to log information, either in a text file, or by printing them to the command prompt.
init_grid_path: ``str``
This attributes store the path where the powergrid data are located. If a relative path is given, it will be
extended as an absolute path.
names_chronics_to_backend: ``dict``
See description of :func:`grid2op.ChronicsHelper.initialize` for more information about this dictionnary
parameters_path: ``str``, optional
Where to look for the :class:`grid2op.Environment` :class:`grid2op.Parameters`. It defaults to ``None`` which
corresponds to using default values.
parameters: :class:`grid2op.Parameters`
Type of _parameters used. This is an instance (object) of type :class:`grid2op.Parameters` initialized from
:attr:`Runner.parameters_path`
path_chron: ``str``
Path indicatng where to look for temporal data.
chronics_handler: :class:`grid2op.ChronicsHandler`
Initialized from :attr:`Runner.gridStateclass` and :attr:`Runner.path_chron` it represents the input data used
to generate grid state by the :attr:`Runner.env`
backend: :class:`grid2op.Backend`
Used to compute the powerflow. This object has the type given by :attr:`Runner.backendClass`
env: :class:`grid2op.Environment`
Represents the environment which the agent / bot / control must control through action. It is initialized from
the :attr:`Runner.envClass`
agent: :class:`grid2op.Agent`
Represents the agent / bot / controler that takes action performed on a environment (the powergrid) to maximize
a certain reward.
verbose: ``bool``
If ``True`` then detailed output of each steps are written.
gridStateclass_kwargs: ``dict``
Additional keyword arguments used to build the :attr:`Runner.chronics_handler`
thermal_limit_a: ``numpy.ndarray``
The thermal limit for the environment (if any).
opponent_action_class: ``type``, optional
The action class used for the opponent. The opponent will not be able to use action that are invalid with
the given action class provided. It defaults to :class:`grid2op.Action.DontAct` which forbid any type
of action possible.
opponent_class: ``type``, optional
The opponent class to use. The default class is :class:`grid2op.Opponent.BaseOpponent` which is a type
of opponents that does nothing.
opponent_init_budget: ``float``, optional
The initial budget of the opponent. It defaults to 0.0 which means the opponent cannot perform any action
if this is not modified.
opponent_budget_per_ts: ``float``, optional
The budget increase of the opponent per time step
opponent_budget_class: ``type``, optional
The class used to compute the attack cost.
grid_layout: ``dict``, optional
The layout of the grid (position of each substation) usefull if you need to plot some things for example.
TODO
_attention_budget_cls=LinearAttentionBudget,
_kwargs_attention_budget=None,
_has_attention_budget=False
Examples
--------
Different examples are showed in the description of the main method :func:`Runner.run`
Notes
-----
Runner does not necessarily behave normally when "nb_process" is not 1 on some platform (windows and some
version of macos). Please read the documentation, and especially the :ref:`runner-multi-proc-warning`
for more information and possible way to disable this feature.
"""
FORCE_SEQUENTIAL = "GRID2OP_RUNNER_FORCE_SEQUENTIAL"
[docs] def __init__(
self,
init_env_path: str,
init_grid_path: str,
path_chron, # path where chronics of injections are stored
n_busbar=2,
name_env="unknown",
parameters_path=None,
names_chronics_to_backend=None,
actionClass=TopologyAction,
observationClass=CompleteObservation,
rewardClass=FlatReward,
legalActClass=AlwaysLegal,
envClass=Environment,
other_env_kwargs=None,
gridStateclass=GridStateFromFile,
# type of chronics to use. For example GridStateFromFile if forecasts are not used,
# or GridStateFromFileWithForecasts otherwise
backendClass=PandaPowerBackend,
backend_kwargs=None,
agentClass=DoNothingAgent, # class used to build the agent
agentInstance=None,
verbose=False,
gridStateclass_kwargs={},
voltageControlerClass=ControlVoltageFromFile,
thermal_limit_a=None,
max_iter=-1,
other_rewards={},
opponent_space_type=OpponentSpace,
opponent_action_class=DontAct,
opponent_class=BaseOpponent,
opponent_init_budget=0.0,
opponent_budget_per_ts=0.0,
opponent_budget_class=NeverAttackBudget,
opponent_attack_duration=0,
opponent_attack_cooldown=99999,
opponent_kwargs={},
grid_layout=None,
with_forecast=True,
attention_budget_cls=LinearAttentionBudget,
kwargs_attention_budget=None,
has_attention_budget=False,
logger=None,
kwargs_observation=None,
observation_bk_class=None,
observation_bk_kwargs=None,
# experimental: whether to read from local dir or generate the classes on the fly:
_read_from_local_dir=False,
_is_test=False, # TODO not implemented !!
):
"""
Initialize the Runner.
Parameters
----------
init_grid_path: ``str``
Madantory, used to initialize :attr:`Runner.init_grid_path`.
path_chron: ``str``
Madantory where to look for chronics data, used to initialize :attr:`Runner.path_chron`.
parameters_path: ``str`` or ``dict``, optional
Used to initialize :attr:`Runner.parameters_path`. If it's a string, this will suppose parameters are
located at this path, if it's a dictionary, this will use the parameters converted from this dictionary.
names_chronics_to_backend: ``dict``, optional
Used to initialize :attr:`Runner.names_chronics_to_backend`.
actionClass: ``type``, optional
Used to initialize :attr:`Runner.actionClass`.
observationClass: ``type``, optional
Used to initialize :attr:`Runner.observationClass`.
rewardClass: ``type``, optional
Used to initialize :attr:`Runner.rewardClass`. Default to :class:`grid2op.ConstantReward` that
*should not** be used to train or evaluate an agent, but rather as debugging purpose.
legalActClass: ``type``, optional
Used to initialize :attr:`Runner.legalActClass`.
envClass: ``type``, optional
Used to initialize :attr:`Runner.envClass`.
gridStateclass: ``type``, optional
Used to initialize :attr:`Runner.gridStateclass`.
backendClass: ``type``, optional
Used to initialize :attr:`Runner.backendClass`.
agentClass: ``type``, optional
Used to initialize :attr:`Runner.agentClass`.
agentInstance: :class:`grid2op.Agent.Agent`
Used to initialize the agent. Note that either :attr:`agentClass` or :attr:`agentInstance` is used
at the same time. If both ot them are ``None`` or both of them are "not ``None``" it throw an error.
verbose: ``bool``, optional
Used to initialize :attr:`Runner.verbose`.
thermal_limit_a: ``numpy.ndarray``
The thermal limit for the environment (if any).
voltagecontrolerClass: :class:`grid2op.VoltageControler.ControlVoltageFromFile`, optional
The controler that will change the voltage setpoints of the generators.
# TODO documentation on the opponent
# TOOD doc for the attention budget
"""
self._n_busbar = n_busbar
self.with_forecast = with_forecast
self.name_env = name_env
if not isinstance(envClass, type):
raise Grid2OpException(
'Parameter "envClass" used to build the Runner should be a type (a class) and not an object '
'(an instance of a class). It is currently "{}"'.format(type(envClass))
)
if not issubclass(envClass, Environment):
raise RuntimeError(
"Impossible to create a runner without an evnrionment derived from grid2op.Environement"
' class. Please modify "envClass" parameter.'
)
self.envClass = envClass
if other_env_kwargs is not None:
self.other_env_kwargs = other_env_kwargs
else:
self.other_env_kwargs = {}
if not isinstance(actionClass, type):
raise Grid2OpException(
'Parameter "actionClass" used to build the Runner should be a type (a class) and not an object '
'(an instance of a class). It is currently "{}"'.format(
type(actionClass)
)
)
if not issubclass(actionClass, BaseAction):
raise RuntimeError(
"Impossible to create a runner without an action class derived from grid2op.BaseAction. "
'Please modify "actionClass" parameter.'
)
self.actionClass = actionClass
if not isinstance(observationClass, type):
raise Grid2OpException(
'Parameter "observationClass" used to build the Runner should be a type (a class) and not an object '
'(an instance of a class). It is currently "{}"'.format(
type(observationClass)
)
)
if not issubclass(observationClass, BaseObservation):
raise RuntimeError(
"Impossible to create a runner without an observation class derived from "
'grid2op.BaseObservation. Please modify "observationClass" parameter.'
)
self.observationClass = observationClass
if isinstance(rewardClass, type):
if not issubclass(rewardClass, BaseReward):
raise RuntimeError(
"Impossible to create a runner without an observation class derived from "
'grid2op.BaseReward. Please modify "rewardClass" parameter.'
)
else:
if not isinstance(rewardClass, BaseReward):
raise RuntimeError(
"Impossible to create a runner without an observation class derived from "
'grid2op.BaseReward. Please modify "rewardClass" parameter.'
)
self.rewardClass = rewardClass
if not isinstance(gridStateclass, type):
raise Grid2OpException(
'Parameter "gridStateclass" used to build the Runner should be a type (a class) and not an object '
'(an instance of a class). It is currently "{}"'.format(
type(gridStateclass)
)
)
if not issubclass(gridStateclass, GridValue):
raise RuntimeError(
"Impossible to create a runner without an chronics class derived from "
'grid2op.GridValue. Please modify "gridStateclass" parameter.'
)
self.gridStateclass = gridStateclass
self.envClass._check_rules_correct(legalActClass)
self.legalActClass = legalActClass
if not isinstance(backendClass, type):
raise Grid2OpException(
'Parameter "legalActClass" used to build the Runner should be a type (a class) and not an object '
'(an instance of a class). It is currently "{}"'.format(
type(backendClass)
)
)
if not issubclass(backendClass, Backend):
raise RuntimeError(
"Impossible to create a runner without a backend class derived from grid2op.GridValue. "
'Please modify "backendClass" parameter.'
)
self.backendClass = backendClass
if backend_kwargs is not None:
self._backend_kwargs = backend_kwargs
else:
self._backend_kwargs = {}
self.__can_copy_agent = True
if agentClass is not None:
if agentInstance is not None:
raise RuntimeError(
"Impossible to build the Runner. Only one of agentClass or agentInstance can be "
"used (both are set / both are not None)."
)
if not isinstance(agentClass, type):
raise Grid2OpException(
'Parameter "agentClass" used to build the Runner should be a type (a class) and not an object '
'(an instance of a class). It is currently "{}"'.format(
type(agentClass)
)
)
if not issubclass(agentClass, BaseAgent):
raise RuntimeError(
"Impossible to create a runner without an agent class derived from "
"grid2op.BaseAgent. "
'Please modify "agentClass" parameter.'
)
self.agentClass = agentClass
self._useclass = True
self.agent = None
elif agentInstance is not None:
if not isinstance(agentInstance, BaseAgent):
raise RuntimeError(
"Impossible to create a runner without an agent class derived from "
"grid2op.BaseAgent. "
'Please modify "agentInstance" parameter.'
)
self.agentClass = None
self._useclass = False
self.agent = agentInstance
# Test if we can copy the agent for parallel runs
try:
copy.copy(self.agent)
except Exception as exc_:
self.__can_copy_agent = False
else:
raise RuntimeError(
"Impossible to build the backend. Either AgentClass or agentInstance must be provided "
"and both are None."
)
self.agentInstance = agentInstance
self._read_from_local_dir = _read_from_local_dir
self._observation_bk_class = observation_bk_class
self._observation_bk_kwargs = observation_bk_kwargs
self.logger = ConsoleLog(DoNothingLog.INFO if verbose else DoNothingLog.ERROR)
if logger is None:
import logging
self.logger = logging.getLogger(__name__)
if verbose:
self.logger.setLevel("debug")
else:
self.logger.disabled = True
else:
self.logger = logger.getChild("grid2op_Runner")
# store _parameters
self.init_env_path = init_env_path
self.init_grid_path = init_grid_path
self.names_chronics_to_backend = names_chronics_to_backend
# game _parameters
self.parameters_path = parameters_path
if isinstance(parameters_path, str):
self.parameters = Parameters(parameters_path)
elif isinstance(parameters_path, dict):
self.parameters = Parameters()
self.parameters.init_from_dict(parameters_path)
elif parameters_path is None:
self.parameters = Parameters()
else:
raise RuntimeError(
'Impossible to build the parameters. The argument "parameters_path" should either '
"be a string or a dictionary."
)
# chronics of grid state
self.path_chron = path_chron
self.gridStateclass_kwargs = gridStateclass_kwargs
self.max_iter = max_iter
if max_iter > 0:
self.gridStateclass_kwargs["max_iter"] = max_iter
self.chronics_handler = ChronicsHandler(
chronicsClass=self.gridStateclass,
path=self.path_chron,
**self.gridStateclass_kwargs
)
self.verbose = verbose
self.thermal_limit_a = thermal_limit_a
# controler for voltage
if not issubclass(voltageControlerClass, ControlVoltageFromFile):
raise Grid2OpException(
'Parameter "voltagecontrolClass" should derive from "ControlVoltageFromFile".'
)
self.voltageControlerClass = voltageControlerClass
self._other_rewards = other_rewards
# for opponent (should be defined here) after the initialization of BaseEnv
self._opponent_space_type = opponent_space_type
if not issubclass(opponent_action_class, BaseAction):
raise EnvError(
"Impossible to make an environment with an opponent action class not "
"derived from BaseAction"
)
try:
self.opponent_init_budget = dt_float(opponent_init_budget)
except Exception as e:
raise EnvError(
'Impossible to convert "opponent_init_budget" to a float with error {}'.format(
e
)
)
if self.opponent_init_budget < 0.0:
raise EnvError(
"If you want to deactive the opponent, please don't set its budget to a negative number."
'Prefer the use of the DontAct action type ("opponent_action_class=DontAct" '
"and / or set its budget to 0."
)
if not issubclass(opponent_class, BaseOpponent):
raise EnvError(
"Impossible to make an opponent with a type that does not inherit from BaseOpponent."
)
self.opponent_action_class = opponent_action_class
self.opponent_class = opponent_class
self.opponent_init_budget = opponent_init_budget
self.opponent_budget_per_ts = opponent_budget_per_ts
self.opponent_budget_class = opponent_budget_class
self.opponent_attack_duration = opponent_attack_duration
self.opponent_attack_cooldown = opponent_attack_cooldown
self.opponent_kwargs = opponent_kwargs
self.grid_layout = grid_layout
# attention budget
self._attention_budget_cls = attention_budget_cls
self._kwargs_attention_budget = copy.deepcopy(kwargs_attention_budget)
self._has_attention_budget = has_attention_budget
# custom observation building
if kwargs_observation is None:
kwargs_observation = {}
self._kwargs_observation = copy.deepcopy(kwargs_observation)
# otherwise on windows / macos it sometimes fail in the runner in multi process
# on linux like OS i prefer to generate all the proper classes accordingly
if _IS_LINUX:
pass
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with self.init_env() as env:
bk_class = type(env.backend)
pass
# not implemented !
self._is_test = _is_test
self.__used = False
def _new_env(self, chronics_handler, parameters) -> Tuple[BaseEnv, BaseAgent]:
# the same chronics_handler is used for all the environments.
# make sure to "reset" it properly
# (this is handled elsewhere in case of "multi chronics")
if not self.chronics_handler.chronicsClass.MULTI_CHRONICS:
self.chronics_handler.next_chronics()
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
res = self.envClass.init_obj_from_kwargs(
n_busbar=self._n_busbar,
other_env_kwargs=self.other_env_kwargs,
init_env_path=self.init_env_path,
init_grid_path=self.init_grid_path,
chronics_handler=chronics_handler,
backend=self.backendClass(**self._backend_kwargs),
parameters=parameters,
name=self.name_env,
names_chronics_to_backend=self.names_chronics_to_backend,
actionClass=self.actionClass,
observationClass=self.observationClass,
rewardClass=self.rewardClass,
legalActClass=self.legalActClass,
voltagecontrolerClass=self.voltageControlerClass,
other_rewards=self._other_rewards,
opponent_space_type=self._opponent_space_type,
opponent_action_class=self.opponent_action_class,
opponent_class=self.opponent_class,
opponent_init_budget=self.opponent_init_budget,
opponent_budget_per_ts=self.opponent_budget_per_ts,
opponent_budget_class=self.opponent_budget_class,
opponent_attack_duration=self.opponent_attack_duration,
opponent_attack_cooldown=self.opponent_attack_cooldown,
kwargs_opponent=self.opponent_kwargs,
with_forecast=self.with_forecast,
attention_budget_cls=self._attention_budget_cls,
kwargs_attention_budget=self._kwargs_attention_budget,
has_attention_budget=self._has_attention_budget,
logger=self.logger,
kwargs_observation=self._kwargs_observation,
observation_bk_class=self._observation_bk_class,
observation_bk_kwargs=self._observation_bk_kwargs,
_raw_backend_class=self.backendClass,
_read_from_local_dir=self._read_from_local_dir,
)
if self.thermal_limit_a is not None:
res.set_thermal_limit(self.thermal_limit_a)
if self.grid_layout is not None:
res.attach_layout(self.grid_layout)
if self._useclass:
agent = self.agentClass(res.action_space)
else:
if self.__can_copy_agent:
agent = copy.copy(self.agent)
else:
agent = self.agent
return res, agent
[docs] def init_env(self) -> BaseEnv:
"""
INTERNAL
.. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\
Function used to initialized the environment and the agent.
It is called by :func:`Runner.reset`.
"""
env, self.agent = self._new_env(self.chronics_handler, self.parameters)
return env
[docs] def reset(self):
"""
INTERNAL
.. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\
Used to reset an environment. This method is called at the beginning of each new episode.
If the environment is not initialized, then it initializes it with :func:`Runner.make_env`.
"""
pass
[docs] def run_one_episode(
self,
indx=0,
path_save=None,
pbar=False,
env_seed=None,
max_iter=None,
agent_seed=None,
episode_id=None,
detailed_output=False,
add_nb_highres_sim=False,
) -> runner_returned_type:
"""
INTERNAL
.. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\
Function used to run one episode of the :attr:`Runner.agent` and see how it performs in the :attr:`Runner.env`.
Parameters
----------
indx: ``int``
The number of episode previously run
path_save: ``str``, optional
Path where to save the data. See the description of :mod:`grid2op.Runner` for the structure of the saved
file.
detailed_output:
See descr. of :func:`Runner.run` method
add_nb_highres_sim:
See descr. of :func:`Runner.run` method
Returns
-------
TODO DEPRECATED DOC
cum_reward: ``np.float32``
The cumulative reward obtained by the agent during this episode
time_step: ``int``
The number of timesteps that have been played before the end of the episode (because of a "game over" or
because there were no more data)
"""
self.reset()
with self.init_env() as env:
res = _aux_run_one_episode(
env,
self.agent,
self.logger,
indx,
path_save,
pbar=pbar,
env_seed=env_seed,
max_iter=max_iter,
agent_seed=agent_seed,
detailed_output=detailed_output,
)
if max_iter is not None:
env.chronics_handler.set_max_iter(-1)
# `res` here necessarily contains detailed_output and nb_highres_call
if not add_nb_highres_sim:
res = res[:-1]
if not detailed_output:
res = res[:-1]
return res
[docs] def _run_sequential(
self,
nb_episode,
path_save=None,
pbar=False,
env_seeds=None,
agent_seeds=None,
max_iter=None,
episode_id=None,
add_detailed_output=False,
add_nb_highres_sim=False,
) -> List[runner_returned_type]:
"""
INTERNAL
.. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\
This method is called to see how well an agent performed on a sequence of episode.
Parameters
----------
nb_episode: ``int``
Number of episode to play.
path_save: ``str``, optional
If not None, it specifies where to store the data. See the description of this module :mod:`Runner` for
more information
pbar: ``bool`` or ``type`` or ``object``
How to display the progress bar, understood as follow:
- if pbar is ``None`` nothing is done.
- if pbar is a boolean, tqdm pbar are used, if tqdm package is available and installed on the system
[if ``true``]. If it's false it's equivalent to pbar being ``None``
- if pbar is a ``type`` ( a class), it is used to build a progress bar at the highest level (episode) and
and the lower levels (step during the episode). If it's a type it muyst accept the argument "total"
and "desc" when being built, and the closing is ensured by this method.
- if pbar is an object (an instance of a class) it is used to make a progress bar at this highest level
(episode) but not at lower levels (setp during the episode)
env_seeds: ``list``
An iterable of the seed used for the experiments. By default ``None``, no seeds are set. If provided,
its size should match ``nb_episode``.
episode_id: ``list``
For each of the nb_episdeo you want to compute, it specifies the id of the chronix that will be used.
By default ``None``, no seeds are set. If provided,
its size should match ``nb_episode``.
add_detailed_output: see Runner.run method
Returns
-------
res: ``list``
List of tuple. Each tuple having 5 elements:
- "id_chron" unique identifier of the episode
- "name_chron" name of chronics
- "cum_reward" the cumulative reward obtained by the :attr:`Runner.BaseAgent` on this episode i
- "nb_time_step": the number of time steps played in this episode.
- "max_ts" : the maximum number of time steps of the chronics
- "episode_data" : The :class:`EpisodeData` corresponding to this episode run
"""
res = [(None, None, None, None, None, None)
for _ in range(nb_episode)]
next_pbar = [False]
with _aux_make_progress_bar(pbar, nb_episode, next_pbar) as pbar_:
for i in range(nb_episode):
env_seed = None
if env_seeds is not None:
env_seed = env_seeds[i]
agt_seed = None
if agent_seeds is not None:
agt_seed = agent_seeds[i]
ep_id = i # if no "episode_id" is provided i used the i th one
if episode_id is not None:
ep_id = episode_id[i] # otherwise i use the provided one
(
name_chron,
cum_reward,
nb_time_step,
max_ts,
episode_data,
nb_call_highres_sim,
) = self.run_one_episode(
path_save=path_save,
indx=ep_id,
pbar=next_pbar[0],
env_seed=env_seed,
agent_seed=agt_seed,
max_iter=max_iter,
detailed_output=True,
add_nb_highres_sim=True
)
id_chron = self.chronics_handler.get_id()
res[i] = (id_chron,
name_chron,
float(cum_reward),
nb_time_step,
max_ts
)
if add_detailed_output:
res[i] = (*res[i], episode_data)
if add_nb_highres_sim:
res[i] = (*res[i], nb_call_highres_sim)
pbar_.update(1)
return res
[docs] def _run_parrallel(
self,
nb_episode,
nb_process=1,
path_save=None,
env_seeds=None,
agent_seeds=None,
max_iter=None,
episode_id=None,
add_detailed_output=False,
add_nb_highres_sim=False,
) -> List[runner_returned_type]:
"""
INTERNAL
.. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\
This method will run in parallel, independently the nb_episode over nb_process.
In case the agent cannot be cloned using `copy.copy`: nb_process is set to 1
Note that it restarts completely the :attr:`Runner.backend` and :attr:`Runner.env` if the computation
is actually performed with more than 1 cores (nb_process > 1)
It uses the python multiprocess, and especially the :class:`multiprocess.Pool` to perform the computations.
This implies that all runs are completely independent (they happen in different process) and that the
memory consumption can be big. Tests may be recommended if the amount of RAM is low.
It has the same return type as the :func:`Runner.run_sequential`.
Parameters
----------
nb_episode: ``int``
Number of episode to simulate
nb_process: ``int``, optional
Number of process used to play the nb_episode. Default to 1.
path_save: ``str``, optional
If not None, it specifies where to store the data. See the description of this module :mod:`Runner` for
more information
env_seeds: ``list``
An iterable of the seed used for the experiments. By default ``None``, no seeds are set. If provided,
its size should match ``nb_episode``.
agent_seeds: ``list``
An iterable that contains the seed used for the environment. By default ``None`` means no seeds are set.
If provided, its size should match the ``nb_episode``. The agent will be seeded at the beginning of each
scenario BEFORE calling `agent.reset()`.
add_detailed_output: see Runner.run method
Returns
-------
res: ``list``
List of tuple. Each tuple having 3 elements:
- "i" unique identifier of the episode (compared to :func:`Runner.run_sequential`, the elements of the
returned list are not necessarily sorted by this value)
- "cum_reward" the cumulative reward obtained by the :attr:`Runner.BaseAgent` on this episode i
- "nb_time_step": the number of time steps played in this episode.
- "max_ts" : the maximum number of time steps of the chronics
- "episode_data" : The :class:`EpisodeData` corresponding to this episode run
"""
if nb_process <= 0:
raise RuntimeError("Runner: you need at least 1 process to run episodes")
force_sequential = False
tmp = os.getenv(Runner.FORCE_SEQUENTIAL)
if tmp is not None:
force_sequential = int(tmp) > 0
if nb_process == 1 or (not self.__can_copy_agent) or force_sequential:
# on windows if i start using sequential, i need to continue using sequential
# if i start using parallel i need to continue using parallel
# so i force the usage of the sequential mode
self.logger.warn(
"Runner.run_parrallel: number of process set to 1. Failing back into sequential mod."
)
return self._run_sequential(
nb_episode,
path_save=path_save,
env_seeds=env_seeds,
max_iter=max_iter,
agent_seeds=agent_seeds,
episode_id=episode_id,
add_detailed_output=add_detailed_output,
add_nb_highres_sim=add_nb_highres_sim,
)
else:
self._clean_up()
nb_process = int(nb_process)
process_ids = [[] for i in range(nb_process)]
for i in range(nb_episode):
if episode_id is None:
process_ids[i % nb_process].append(i)
else:
process_ids[i % nb_process].append(episode_id[i])
if env_seeds is None:
seeds_env_res = [None for _ in range(nb_process)]
else:
# split the seeds according to the process
seeds_env_res = [[] for i in range(nb_process)]
for i in range(nb_episode):
seeds_env_res[i % nb_process].append(env_seeds[i])
if agent_seeds is None:
seeds_agt_res = [None for _ in range(nb_process)]
else:
# split the seeds according to the process
seeds_agt_res = [[] for i in range(nb_process)]
for i in range(nb_episode):
seeds_agt_res[i % nb_process].append(agent_seeds[i])
res = []
if _IS_LINUX:
lists = [(self,) for _ in enumerate(process_ids)]
else:
lists = [(Runner(**self._get_params()),) for _ in enumerate(process_ids)]
for i, pn in enumerate(process_ids):
lists[i] = (*lists[i],
pn,
i,
path_save,
seeds_env_res[i],
seeds_agt_res[i],
max_iter,
add_detailed_output,
add_nb_highres_sim)
with Pool(nb_process) as p:
tmp = p.starmap(_aux_one_process_parrallel, lists)
for el in tmp:
res += el
return res
def _get_params(self):
res = {
"init_grid_path": self.init_grid_path,
"init_env_path": self.init_env_path,
"path_chron": self.path_chron, # path where chronics of injections are stored
"name_env": self.name_env,
"parameters_path": self.parameters_path,
"names_chronics_to_backend": self.names_chronics_to_backend,
"actionClass": self.actionClass,
"observationClass": self.observationClass,
"rewardClass": self.rewardClass,
"legalActClass": self.legalActClass,
"envClass": self.envClass,
"gridStateclass": self.gridStateclass,
"backendClass": self.backendClass,
"backend_kwargs": self._backend_kwargs,
"agentClass": self.agentClass,
"agentInstance": self.agentInstance,
"verbose": self.verbose,
"gridStateclass_kwargs": copy.deepcopy(self.gridStateclass_kwargs),
"voltageControlerClass": self.voltageControlerClass,
"thermal_limit_a": self.thermal_limit_a,
"max_iter": self.max_iter,
"other_rewards": copy.deepcopy(self._other_rewards),
"opponent_space_type": self._opponent_space_type,
"opponent_action_class": self.opponent_action_class,
"opponent_class": self.opponent_class,
"opponent_init_budget": self.opponent_init_budget,
"opponent_budget_per_ts": self.opponent_budget_per_ts,
"opponent_budget_class": self.opponent_budget_class,
"opponent_attack_duration": self.opponent_attack_duration,
"opponent_attack_cooldown": self.opponent_attack_cooldown,
"opponent_kwargs": copy.deepcopy(self.opponent_kwargs),
"grid_layout": copy.deepcopy(self.grid_layout),
"with_forecast": self.with_forecast,
"attention_budget_cls": self._attention_budget_cls,
"kwargs_attention_budget": self._kwargs_attention_budget,
"has_attention_budget": self._has_attention_budget,
"logger": self.logger,
"kwargs_observation": self._kwargs_observation,
"_read_from_local_dir": self._read_from_local_dir,
"_is_test": self._is_test,
}
return res
[docs] def _clean_up(self):
"""
INTERNAL
.. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\
close the environment if it has been created
"""
pass
[docs] def run(
self,
nb_episode,
nb_process=1,
path_save=None,
max_iter=None,
pbar=False,
env_seeds=None,
agent_seeds=None,
episode_id=None,
add_detailed_output=False,
add_nb_highres_sim=False,
) -> List[runner_returned_type]:
"""
Main method of the :class:`Runner` class. It will either call :func:`Runner._run_sequential` if "nb_process" is
1 or :func:`Runner._run_parrallel` if nb_process >= 2.
Parameters
----------
nb_episode: ``int``
Number of episode to simulate
nb_process: ``int``, optional
Number of process used to play the nb_episode. Default to 1. **NB** Multitoprocessing is deactivated
on windows based platform (it was not fully supported so we decided to remove it)
path_save: ``str``, optional
If not None, it specifies where to store the data. See the description of this module :mod:`Runner` for
more information
max_iter: ``int``
Maximum number of iteration you want the runner to perform.
pbar: ``bool`` or ``type`` or ``object``
How to display the progress bar, understood as follow:
- if pbar is ``None`` nothing is done.
- if pbar is a boolean, tqdm pbar are used, if tqdm package is available and installed on the system
[if ``true``]. If it's false it's equivalent to pbar being ``None``
- if pbar is a ``type`` ( a class), it is used to build a progress bar at the highest level (episode) and
and the lower levels (step during the episode). If it's a type it muyst accept the argument "total"
and "desc" when being built, and the closing is ensured by this method.
- if pbar is an object (an instance of a class) it is used to make a progress bar at this highest level
(episode) but not at lower levels (setp during the episode)
env_seeds: ``list``
An iterable of the seed used for the environment. By default ``None``, no seeds are set. If provided,
its size should match ``nb_episode``.
agent_seeds: ``list``
An iterable that contains the seed used for the environment. By default ``None`` means no seeds are set.
If provided, its size should match the ``nb_episode``. The agent will be seeded at the beginning of each
scenario BEFORE calling `agent.reset()`.
episode_id: ``list``
For each of the nb_episdeo you want to compute, it specifies the id of the chronix that will be used.
By default ``None``, no seeds are set. If provided,
its size should match ``nb_episode``.
add_detailed_output: ``bool``
A flag to add an :class:`EpisodeData` object to the results, containing a lot of information about the run
add_nb_highres_sim: ``bool``
Whether to add an estimated number of "high resolution simulator" called performed by the agent (either by
obs.simulate, or by obs.get_forecast_env or by obs.get_simulator)
Returns
-------
res: ``list``
List of tuple. Each tuple having 3[4] elements:
- "i" unique identifier of the episode (compared to :func:`Runner.run_sequential`, the elements of the
returned list are not necessarily sorted by this value)
- "cum_reward" the cumulative reward obtained by the :attr:`Runner.Agent` on this episode i
- "nb_time_step": the number of time steps played in this episode.
- "total_step": the total number of time steps possible in this episode.
- "episode_data" : [Optional] The :class:`EpisodeData` corresponding to this episode run only
if `add_detailed_output=True`
- "add_nb_highres_sim": [Optional] The estimated number of calls to high resolution simulator made
by the agent
Examples
--------
You can use the runner this way:
.. code-block: python
import grid2op
from gri2op.Runner import Runner
from grid2op.Agent import RandomAgent
env = grid2op.make("l2rpn_case14_sandbox")
runner = Runner(**env.get_params_for_runner(), agentClass=RandomAgent)
res = runner.run(nb_episode=1)
If you would rather to provide an agent instance (and not a class) you can do it this way:
.. code-block: python
import grid2op
from gri2op.Runner import Runner
from grid2op.Agent import RandomAgent
env = grid2op.make("l2rpn_case14_sandbox")
my_agent = RandomAgent(env.action_space)
runner = Runner(**env.get_params_for_runner(), agentClass=None, agentInstance=my_agent)
res = runner.run(nb_episode=1)
Finally, in the presence of stochastic environments or stochastic agent you might want to set the seeds for
ensuring reproducible experiments you might want to seed both the environment and your agent. You can do that
by passing `env_seeds` and `agent_seeds` parameters (on the example bellow, the agent will be seeded with 42
and the environment with 0.
.. code-block: python
import grid2op
from gri2op.Runner import Runner
from grid2op.Agent import RandomAgent
env = grid2op.make("l2rpn_case14_sandbox")
my_agent = RandomAgent(env.action_space)
runner = Runner(**env.get_params_for_runner(), agentClass=None, agentInstance=my_agent)
res = runner.run(nb_episode=1, agent_seeds=[42], env_seeds=[0])
"""
if nb_episode < 0:
raise RuntimeError("Impossible to run a negative number of scenarios.")
if env_seeds is not None:
if len(env_seeds) != nb_episode:
raise RuntimeError(
'You want to compute "{}" run(s) but provide only "{}" different seeds '
"(environment)."
"".format(nb_episode, len(env_seeds))
)
if agent_seeds is not None:
if len(agent_seeds) != nb_episode:
raise RuntimeError(
'You want to compute "{}" run(s) but provide only "{}" different seeds (agent).'
"".format(nb_episode, len(agent_seeds))
)
if episode_id is not None:
if len(episode_id) != nb_episode:
raise RuntimeError(
'You want to compute "{}" run(s) but provide only "{}" different ids.'
"".format(nb_episode, len(episode_id))
)
if max_iter is not None:
max_iter = int(max_iter)
if nb_episode == 0:
res = []
else:
try:
if nb_process <= 0:
raise RuntimeError("Impossible to run using less than 1 process.")
self.__used = True
if nb_process == 1:
self.logger.info("Sequential runner used.")
res = self._run_sequential(
nb_episode,
path_save=path_save,
pbar=pbar,
env_seeds=env_seeds,
max_iter=max_iter,
agent_seeds=agent_seeds,
episode_id=episode_id,
add_detailed_output=add_detailed_output,
add_nb_highres_sim=add_nb_highres_sim,
)
else:
if add_detailed_output and (_IS_WINDOWS or _IS_MACOS):
self.logger.warn(
"Parallel run are not fully supported on windows or macos when "
'"add_detailed_output" is True. So we decided '
"to fully deactivate them."
)
res = self._run_sequential(
nb_episode,
path_save=path_save,
pbar=pbar,
env_seeds=env_seeds,
max_iter=max_iter,
agent_seeds=agent_seeds,
episode_id=episode_id,
add_detailed_output=add_detailed_output,
add_nb_highres_sim=add_nb_highres_sim,
)
else:
self.logger.info("Parallel runner used.")
res = self._run_parrallel(
nb_episode,
nb_process=nb_process,
path_save=path_save,
env_seeds=env_seeds,
max_iter=max_iter,
agent_seeds=agent_seeds,
episode_id=episode_id,
add_detailed_output=add_detailed_output,
add_nb_highres_sim=add_nb_highres_sim,
)
finally:
self._clean_up()
return res