Runner.py

# 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 time
import warnings

import sys
import numpy as np
import copy

from multiprocessing import Pool

from grid2op.dtypes import dt_int, dt_float, dt_bool
from grid2op.Action import BaseAction, TopologyAction, DontAct
from grid2op.Exceptions import *
from grid2op.Observation import CompleteObservation, BaseObservation
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.Episode import EpisodeData
from grid2op.Runner.FakePBar import _FakePbar
from grid2op.VoltageControler import ControlVoltageFromFile
from grid2op.dtypes import dt_float
from grid2op.Opponent import BaseOpponent, NeverAttackBudget

# 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
_IS_WINDOWS = sys.platform.startswith('win')

# 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. :-/


class DoNothingLog:
    """
    .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

    A class to emulate the behaviour of a logger, but that does absolutely nothing.
    """
    INFO = 2
    WARNING = 1
    ERROR = 0

    def __init__(self, max_level=2):
        self.max_level = max_level

    def warn(self, *args, **kwargs):
        pass

    def info(self, *args, **kwargs):
        pass

    def error(self, *args, **kwargs):
        pass

    def warning(self, *args, **kwargs):
        pass


class ConsoleLog(DoNothingLog):
    """
    .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

    A class to emulate the behaviour of a logger, but that prints on the console
    """

    def __init__(self, max_level=2):
        DoNothingLog.__init__(self, max_level)

    def warn(self, *args, **kwargs):
        if self.max_level >= self.WARNING:
            if args:
                print("WARNING: \"{}\"".format(", ".join(args)))
            if kwargs:
                print("WARNING: {}".format(kwargs))

    def info(self, *args, **kwargs):
        if self.max_level >= self.INFO:
            if args:
                print("INFO: \"{}\"".format(", ".join(args)))
            if kwargs:
                print("INFO: {}".format(kwargs))

    def error(self, *args, **kwargs):
        if self.max_level >= self.ERROR:
            if args:
                print("ERROR: \"{}\"".format(", ".join(args)))
            if kwargs:
                print("ERROR: {}".format(kwargs))

    def warning(self, *args, **kwargs):
        if self.max_level >= self.WARNING:
            if args:
                print("WARNING: \"{}\"".format(", ".join(args)))
            if kwargs:
                print("WARNING: {}".format(kwargs))


class Runner(object):
    """
    A runner is a utilitary 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()

        ###############
        # 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.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.

    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`.

    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.

    Examples
    --------
    Different examples are showed in the description of the main method :func:`Runner.run`

    """

    def __init__(self,
                 init_grid_path: str,
                 path_chron,  # path where chronics of injections are stored
                 name_env="unknown",
                 parameters_path=None,
                 names_chronics_to_backend=None,
                 actionClass=TopologyAction,
                 observationClass=CompleteObservation,
                 rewardClass=FlatReward,
                 legalActClass=AlwaysLegal,
                 envClass=Environment,
                 gridStateclass=GridStateFromFile,
                 # type of chronics to use. For example GridStateFromFile if forecasts are not used, or GridStateFromFileWithForecasts otherwise
                 backendClass=PandaPowerBackend,
                 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_action_class=DontAct,
                 opponent_class=BaseOpponent,
                 opponent_init_budget=0.,
                 opponent_budget_per_ts=0.,
                 opponent_budget_class=NeverAttackBudget,
                 opponent_attack_duration=0,
                 opponent_attack_cooldown=99999,
                 opponent_kwargs={},
                 grid_layout=None,
                 with_forecast=True):
        """
        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
        """
        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 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 not isinstance(rewardClass, type):
            raise Grid2OpException(
                "Parameter \"rewardClass\" 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(rewardClass)))
    
        if not issubclass(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

        if not isinstance(legalActClass, 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(legalActClass)))
        if not issubclass(legalActClass, BaseRules):

            raise RuntimeError("Impossible to create a runner without a class defining legal actions derived "
                               "from grid2op.BaseRules. Please modify \"legalActClass\" parameter.")
        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

        self.__can_copy_agent = True
        if agentClass is not None:
            if agentInstance is not None:
                raise RuntimeError("Impossible to build the backend. Only one of AgentClass or agentInstance can be "
                                   "used (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:
                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.logger = ConsoleLog(
            DoNothingLog.INFO if verbose else DoNothingLog.ERROR)

        # store _parameters
        self.init_grid_path = init_grid_path
        self.names_chronics_to_backend = names_chronics_to_backend

        # game _parameters
        if isinstance(parameters_path, str):
            self.parameters_path = parameters_path
            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_path = parameters_path
            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)

        # the backend, used to compute powerflows
        self.backend = self.backendClass()

        # build the environment
        self.env = None

        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
        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.:
            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

        # otherwise on windows it sometimes fail in the runner in multi process
        # self.init_env()

    def _new_env(self, chronics_handler, backend, parameters):
        res = self.envClass(init_grid_path=self.init_grid_path,
                            chronics_handler=chronics_handler,
                            backend=backend,
                            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_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,
                            _raw_backend_class=self.backendClass
                            )

        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

    def init_env(self):
        """
        .. 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`.
        """
        self.env, self.agent = self._new_env(self.chronics_handler, self.backend, self.parameters)

    def reset(self):
        """
        .. 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`.
        """
        if self.env is None:
            self.init_env()
        else:
            self.env.reset()

    def run_one_episode(self, indx=0, path_save=None, pbar=False, env_seed=None, max_iter=None, agent_seed=None):
        """
        .. 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.

        Returns
        -------
        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()
        res = self._run_one_episode(self.env, self.agent, self.logger, indx, path_save,
                                    pbar=pbar, env_seed=env_seed, max_iter=max_iter, agent_seed=agent_seed)
        return res

    @staticmethod
    def _run_one_episode(env, agent, logger, indx, path_save=None,
                         pbar=False, env_seed=None, agent_seed=None, max_iter=None):
        done = False
        time_step = int(0)
        time_act = 0.
        cum_reward = dt_float(0.0)

        # reset the environment
        env.chronics_handler.tell_id(indx-1)
        # the "-1" above is because the environment will be reset. So it will increase id of 1.

        # set the seed
        if env_seed is not None:
            env.seed(env_seed)

        # handle max_iter
        if max_iter is not None:
            env.chronics_handler.set_max_iter(max_iter)

        # reset it
        obs = env.reset()

        # seed and reset the agent
        if agent_seed is not None:
            agent.seed(agent_seed)
        agent.reset(obs)

        # compute the size and everything if it needs to be stored
        nb_timestep_max = env.chronics_handler.max_timestep()
        efficient_storing = nb_timestep_max > 0
        nb_timestep_max = max(nb_timestep_max, 0)

        if path_save is None:
            # i don't store anything on drive, so i don't need to store anything on memory
            nb_timestep_max = 0

        disc_lines_templ = np.full(
            (1, env.backend.n_line), fill_value=False, dtype=dt_bool)

        attack_templ = np.full(
            (1, env._oppSpace.action_space.size()), fill_value=0., dtype=dt_float)
        if efficient_storing:
            times = np.full(nb_timestep_max, fill_value=np.NaN, dtype=dt_float)
            rewards = np.full(nb_timestep_max, fill_value=np.NaN, dtype=dt_float)
            actions = np.full((nb_timestep_max, env.action_space.n),
                              fill_value=np.NaN, dtype=dt_float)
            env_actions = np.full(
                (nb_timestep_max, env._helper_action_env.n), fill_value=np.NaN, dtype=dt_float)
            observations = np.full(
                (nb_timestep_max+1, env.observation_space.n), fill_value=np.NaN, dtype=dt_float)
            disc_lines = np.full(
                (nb_timestep_max, env.backend.n_line), fill_value=np.NaN, dtype=dt_bool)
            attack = np.full((nb_timestep_max, env._opponent_action_space.n), fill_value=0., dtype=dt_float)
        else:
            times = np.full(0, fill_value=np.NaN, dtype=dt_float)
            rewards = np.full(0, fill_value=np.NaN, dtype=dt_float)
            actions = np.full((0, env.action_space.n), fill_value=np.NaN, dtype=dt_float)
            env_actions = np.full((0, env._helper_action_env.n), fill_value=np.NaN, dtype=dt_float)
            observations = np.full((0, env.observation_space.n), fill_value=np.NaN, dtype=dt_float)
            disc_lines = np.full((0, env.backend.n_line), fill_value=np.NaN, dtype=dt_bool)
            attack = np.full((0, env._opponent_action_space.n), fill_value=0., dtype=dt_float)

        if path_save is not None:
            # store observation at timestep 0
            if efficient_storing:
                observations[time_step, :] = obs.to_vect()
            else:
                observations = np.concatenate((observations, obs.to_vect().reshape(1, -1)))

        episode = EpisodeData(actions=actions,
                              env_actions=env_actions,
                              observations=observations,
                              rewards=rewards,
                              disc_lines=disc_lines,
                              times=times,
                              observation_space=env.observation_space,
                              action_space=env.action_space,
                              helper_action_env=env._helper_action_env,
                              path_save=path_save,
                              disc_lines_templ=disc_lines_templ,
                              attack_templ=attack_templ,
                              attack=attack,
                              attack_space=env._opponent_action_space,
                              logger=logger,
                              name=env.chronics_handler.get_name(),
                              other_rewards=[])

        episode.set_parameters(env)

        beg_ = time.time()

        reward = float(env.reward_range[0])
        done = False

        next_pbar = [False]
        with Runner._make_progress_bar(pbar, nb_timestep_max, next_pbar) as pbar_:
            while not done:
                beg__ = time.time()
                act = agent.act(obs, reward, done)
                end__ = time.time()
                time_act += end__ - beg__

                obs, reward, done, info = env.step(act)  # should load the first time stamp
                cum_reward += reward
                time_step += 1
                pbar_.update(1)
                opp_attack = env._oppSpace.last_attack
                episode.incr_store(efficient_storing, time_step, end__ - beg__,
                                   float(reward), env._env_modification,
                                   act, obs, opp_attack,
                                   info)
            end_ = time.time()

        episode.set_meta(env, time_step, float(cum_reward), env_seed, agent_seed)

        li_text = ["Env: {:.2f}s", "\t - apply act {:.2f}s", "\t - run pf: {:.2f}s",
                   "\t - env update + observation: {:.2f}s", "Agent: {:.2f}s", "Total time: {:.2f}s",
                   "Cumulative reward: {:1f}"]
        msg_ = "\n".join(li_text)
        logger.info(msg_.format(
            env._time_apply_act + env._time_powerflow + env._time_extract_obs,
            env._time_apply_act, env._time_powerflow, env._time_extract_obs,
            time_act, end_ - beg_, cum_reward))

        episode.set_episode_times(env, time_act, beg_, end_)

        episode.to_disk()
        name_chron = env.chronics_handler.get_name()

        return name_chron, cum_reward, int(time_step)

    @staticmethod
    def _make_progress_bar(pbar, total, next_pbar):
        """
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        Parameters
        ----------
        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 (step during the episode)
        """
        pbar_ = _FakePbar()
        next_pbar[0] = False

        if isinstance(pbar, bool):
            if pbar:
                try:
                    from tqdm import tqdm
                    pbar_ = tqdm(total=total, desc="episode")
                    next_pbar[0] = True
                except (ImportError, ModuleNotFoundError):
                    pass
        elif isinstance(pbar, type):
            pbar_ = pbar(total=total, desc="episode")
            next_pbar[0] = pbar
        elif isinstance(pbar, object):
            pbar_ = pbar
        return pbar_

    def _run_sequential(self, nb_episode, path_save=None, pbar=False, env_seeds=None, agent_seeds=None, max_iter=None):
        """
        .. 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``.

        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

        """
        res = [(None, None, None, None, None) for _ in range(nb_episode)]

        next_pbar = [False]
        with self._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]
                name_chron, cum_reward, nb_time_step = self.run_one_episode(path_save=path_save,
                                                                            indx=i,
                                                                            pbar=next_pbar[0],
                                                                            env_seed=env_seed,
                                                                            agent_seed=agt_seed,
                                                                            max_iter=max_iter)
                id_chron = self.chronics_handler.get_id()
                max_ts = self.chronics_handler.max_timestep()
                res[i] = (id_chron, name_chron, float(cum_reward), nb_time_step, max_ts)
                pbar_.update(1)
        return res

    @staticmethod
    def _one_process_parrallel(runner, episode_this_process, process_id, path_save=None,
                               env_seeds=None, max_iter=None, agent_seeds=None):
        chronics_handler = ChronicsHandler(chronicsClass=runner.gridStateclass,
                                           path=runner.path_chron,
                                           **runner.gridStateclass_kwargs)
        parameters = copy.deepcopy(runner.parameters)
        backend = runner.backendClass()
        nb_episode_this_process = len(episode_this_process)
        res = [(None, None, None) for _ in range(nb_episode_this_process)]
        for i, p_id in enumerate(episode_this_process):
            env, agent = runner._new_env(chronics_handler=chronics_handler,
                                         backend=backend,
                                         parameters=parameters)
            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]
            name_chron, cum_reward, nb_time_step = Runner._run_one_episode(
                env, agent, runner.logger, p_id, path_save, env_seed=env_seed, max_iter=max_iter, agent_seed=agt_seed)
            id_chron = chronics_handler.get_id()
            max_ts = chronics_handler.max_timestep()
            res[i] = (id_chron, name_chron, float(cum_reward), nb_time_step, max_ts)
        return res

    def _run_parrallel(self, nb_episode, nb_process=1, path_save=None, env_seeds=None, agent_seeds=None, max_iter=None):
        """
        .. 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()`.

        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

        """
        if nb_process <= 0:
            raise RuntimeError(
                "Runner: you need at least 1 process to run episodes")
        if _IS_WINDOWS or nb_process == 1 or self.__can_copy_agent is False:
            # 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, agent_seeds=agent_seeds)
        else:
            self._clean_up()
            self.backend = self.backendClass()

            nb_process = int(nb_process)
            process_ids = [[] for i in range(nb_process)]
            for i in range(nb_episode):
                process_ids[i % nb_process].append(i)

            if env_seeds is None:
                seeds_res = [None for _ in range(nb_process)]
            else:
                # split the seeds according to the process
                seeds_res = [[] for i in range(nb_process)]
                for i in range(nb_episode):
                    seeds_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 = []
            with Pool(nb_process) as p:
                tmp = p.starmap(Runner._one_process_parrallel,
                                [(self, pn, i, path_save, seeds_res[i], max_iter) for i, pn in enumerate(process_ids)])
            for el in tmp:
                res += el
        return res

    def _clean_up(self):
        """
        .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\

        close the environment if it has been created

        """
        if self.env is not None:
            self.env.close()
        self.env = None

    def run(self, nb_episode, nb_process=1, path_save=None, max_iter=None, pbar=False, env_seeds=None, agent_seeds=None):
        """
        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()`.

        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.

        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()
            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()
            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()
            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 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.")

                if _IS_WINDOWS and nb_process > 1:
                    self.logger.warn("Parallel run are not fully supported on windows at the moment. So we decided "
                                     "to fully deactivate them.")
                if nb_process == 1 or _IS_WINDOWS:
                    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)
                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)
            finally:
                self._clean_up()
        return res