Runner¶

This module is here to facilitate the evaluation of agent. It can handles all types of grid2op.BaseAgent.

class grid2op.Runner.ConsoleLog(max_level=2)[source]¶

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

__init__(max_level=2)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class grid2op.Runner.DoNothingLog(max_level=2)[source]¶

A class to emulate the behaviour of a logger, but that does absolutely nothing.

__init__(max_level=2)[source]¶: Initialize self. See help(type(self)) for accurate signature.

__weakref__¶: list of weak references to the object (if defined)

class grid2op.Runner.Runner(init_grid_path: str, path_chron, parameters_path=None, names_chronics_to_backend=None, actionClass=<class 'grid2op.Action.TopologyAction.TopologyAction'>, observationClass=<class 'grid2op.Observation.CompleteObservation.CompleteObservation'>, rewardClass=<class 'grid2op.Reward.FlatReward.FlatReward'>, legalActClass=<class 'grid2op.Rules.AlwaysLegal.AlwaysLegal'>, envClass=<class 'grid2op.Environment.Environment.Environment'>, gridStateclass=<class 'grid2op.Chronics.GridStateFromFile.GridStateFromFile'>, backendClass=<class 'grid2op.Backend.PandaPowerBackend.PandaPowerBackend'>, agentClass=<class 'grid2op.Agent.DoNothing.DoNothingAgent'>, agentInstance=None, verbose=False, gridStateclass_kwargs={}, voltageControlerClass=<class 'grid2op.VoltageControler.ControlVoltageFromFile.ControlVoltageFromFile'>, thermal_limit_a=None, max_iter=-1, other_rewards={}, opponent_action_class=<class 'grid2op.Action.DontAct.DontAct'>, opponent_class=<class 'grid2op.Opponent.BaseOpponent.BaseOpponent'>, opponent_init_budget=0, grid_layout=None)[source]¶

A runner is a utilitary tool that allows to create environment, and run simulations more easily. This specific class as for main purpose to evaluate the performance of a trained grid2op.BaseAgent, rather than to train it. Of course, it is possible to adapt it for a specific training mechanisms. Examples of such will be made available in the future.

envClass¶

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 grid2op.Environment. If modified, it should derived from this class.

Type: type

actionClass¶

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 grid2op.BaseAction. The default is grid2op.TopologyAction.

Type: type

observationClass¶

This type represents the class that will be used to build the grid2op.BaseObservation visible by the grid2op.BaseAgent. As Runner.actionClass, this should be a type, and not and instance (object) of this type. This type should derived from grid2op.BaseObservation. The default is grid2op.CompleteObservation.

Type: type

rewardClass¶

Representes the type used to build the rewards that are given to the BaseAgent. As Runner.actionClass, this should be a type, and not and instance (object) of this type. This type should derived from grid2op.BaseReward. The default is grid2op.ConstantReward that should not be used to train or evaluate an agent, but rather as debugging purpose.

Type: type

gridStateclass¶

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 grid2op.GridStateFromFile and it must be a subclass of grid2op.GridValue.

Type: type

legalActClass¶

This types control the mechanisms to assess if an grid2op.BaseAction is legal. Like every “.*Class” attributes the type should be pass and not an intance (object) of this type. Its default is grid2op.AlwaysLegal and it must be a subclass of grid2op.BaseRules.

Type: type

backendClass¶

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 grid2op.PandaPowerBackend and it must be a subclass of grid2op.Backend.

Type: type

agentClass¶

This types control the type of BaseAgent, eg. the bot / controler that will take 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 grid2op.DoNothingAgent and it must be a subclass of grid2op.BaseAgent.

Type: type

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¶

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.

Type: str

names_chronics_to_backend¶

See description of grid2op.ChronicsHelper.initialize() for more information about this dictionnary

Type: dict

parameters_path¶

Where to look for the grid2op.Environment grid2op.Parameters. It defaults to None which corresponds to using default values.

Type: str, optional

parameters¶

Type of _parameters used. This is an instance (object) of type grid2op.Parameters initialized from Runner.parameters_path

Type: grid2op.Parameters

path_chron¶

Path indicatng where to look for temporal data.

Type: str

chronics_handler¶

Initialized from Runner.gridStateclass and Runner.path_chron it represents the input data used to generate grid state by the Runner.env

Type: grid2op.ChronicsHandler

backend¶

Used to compute the powerflow. This object has the type given by Runner.backendClass

Type: grid2op.Backend

env¶

Represents the environment which the agent / bot / control must control through action. It is initialized from the Runner.envClass

Type: grid2op.Environment

agent¶

Represents the agent / bot / controler that takes action performed on a environment (the powergrid) to maximize a certain reward.

Type: grid2op.Agent

verbose¶

If True then detailed output of each steps are written.

Type: bool

gridStateclass_kwargs¶

Additional keyword arguments used to build the Runner.chronics_handler

Type: dict

thermal_limit_a¶

The thermal limit for the environment (if any).

Type: numpy.ndarray

__init__(init_grid_path: str, path_chron, parameters_path=None, names_chronics_to_backend=None, actionClass=<class 'grid2op.Action.TopologyAction.TopologyAction'>, observationClass=<class 'grid2op.Observation.CompleteObservation.CompleteObservation'>, rewardClass=<class 'grid2op.Reward.FlatReward.FlatReward'>, legalActClass=<class 'grid2op.Rules.AlwaysLegal.AlwaysLegal'>, envClass=<class 'grid2op.Environment.Environment.Environment'>, gridStateclass=<class 'grid2op.Chronics.GridStateFromFile.GridStateFromFile'>, backendClass=<class 'grid2op.Backend.PandaPowerBackend.PandaPowerBackend'>, agentClass=<class 'grid2op.Agent.DoNothing.DoNothingAgent'>, agentInstance=None, verbose=False, gridStateclass_kwargs={}, voltageControlerClass=<class 'grid2op.VoltageControler.ControlVoltageFromFile.ControlVoltageFromFile'>, thermal_limit_a=None, max_iter=-1, other_rewards={}, opponent_action_class=<class 'grid2op.Action.DontAct.DontAct'>, opponent_class=<class 'grid2op.Opponent.BaseOpponent.BaseOpponent'>, opponent_init_budget=0, grid_layout=None)[source]¶

Initialize the Runner.

Parameters

init_grid_path (str) – Madantory, used to initialize Runner.init_grid_path.
path_chron (str) – Madantory where to look for chronics data, used to initialize Runner.path_chron.
parameters_path (str or dict, optional) – Used to initialize 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 Runner.names_chronics_to_backend.
actionClass (type, optional) – Used to initialize Runner.actionClass.
observationClass (type, optional) – Used to initialize Runner.observationClass.
rewardClass (type, optional) – Used to initialize Runner.rewardClass. Default to grid2op.ConstantReward that should not* be used to train or evaluate an agent, but rather as debugging purpose.
legalActClass (type, optional) – Used to initialize Runner.legalActClass.
envClass (type, optional) – Used to initialize Runner.envClass.
gridStateclass (type, optional) – Used to initialize Runner.gridStateclass.
backendClass (type, optional) – Used to initialize Runner.backendClass.
agentClass (type, optional) – Used to initialize Runner.agentClass.
agentInstance (grid2op.Agent.Agent) – Used to initialize the agent. Note that either agentClass or 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 Runner.verbose.
thermal_limit_a (numpy.ndarray) – The thermal limit for the environment (if any).
voltagecontrolerClass (grid2op.VoltageControler.ControlVoltageFromFile, optional) – The controler that will change the voltage setpoints of the generators.

__weakref__¶: list of weak references to the object (if defined)

staticmethod _make_progress_bar(pbar, total, next_pbar)[source]¶

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 (setp during the episode)

init_env()[source]¶

Function used to initialized the environment and the agent. It is called by Runner.reset().

Returns
Return type: None

reset()[source]¶

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 Runner.make_env().

Returns
Return type: None

run(nb_episode, nb_process=1, path_save=None, max_iter=None, pbar=False)[source]¶

Main method of the Runner class. It will either call Runner.run_sequential() if “nb_process” is 1 or 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.
path_save (str, optional) – If not None, it specifies where to store the data. See the description of this module Runner for more information

Returns

res –

List of tuple. Each tuple having 3 elements:

”i” unique identifier of the episode (compared to Runner.run_sequential(), the elements of the returned list are not necessarily sorted by this value)

”cum_reward” the cumulative reward obtained by the Runner.BaseAgent on this episode i

”nb_time_step”: the number of time steps played in this episode.

Return type

list

run_one_episode(indx=0, path_save=None, pbar=False)[source]¶

Function used to run one episode of the Runner.agent and see how it performs in the 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 grid2op.Runner for the structure of the saved file.

Returns

cum_reward (float) – 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)

run_parrallel(nb_episode, nb_process=1, path_save=None)[source]¶

This method will run in parrallel, independantly 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 Runner.backend and Runner.env if the computation is actually performed with more than 1 cores (nb_process > 1)

It uses the python multiprocess, and especially the multiprocess.Pool to perform the computations. This implies that all runs are completely independant (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 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 Runner for more information

Returns

res –

List of tuple. Each tuple having 3 elements:

”i” unique identifier of the episode (compared to Runner.run_sequential(), the elements of the returned list are not necessarily sorted by this value)

”cum_reward” the cumulative reward obtained by the 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

Return type

list

run_sequential(nb_episode, path_save=None, pbar=False)[source]¶

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 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)

Returns

res –

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 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

Return type

list

If you still can’t find what you’re looking for, try in one of the following pages: