class Environment(_BasicEnv):
"""
Attributes
----------
logger: ``logger``
Use to store some information (currently in beta status)
time_stamp: ``datetime.time``
Current time of the chronics
nb_time_step: ``int``
Number of time steps played this episode
parameters: :class:`grid2op.Parameters.Parameters`
Parameters used for the game
rewardClass: ``type``
Type of reward used. Should be a subclass of :class:`grid2op.BaseReward.BaseReward`
init_grid_path: ``str``
The path where the description of the powergrid is located.
backend: :class:`grid2op.Backend.Backend`
The backend used to compute powerflows and cascading failures.
game_rules: :class:`grid2op.GameRules.RulesChecker`
The rules of the game (define which actions are legal and which are not)
helper_action_player: :class:`grid2op.Action.ActionSpace`
Helper used to manipulate more easily the actions given to / provided by the :class:`grid2op.BaseAgent` (player)
helper_action_env: :class:`grid2op.Action.ActionSpace`
Helper used to manipulate more easily the actions given to / provided by the environment to the backend.
helper_observation: :class:`grid2op.Observation.ObservationSpace`
Helper used to generate the observation that will be given to the :class:`grid2op.BaseAgent`
current_obs: :class:`grid2op.Observation.Observation`
The current observation (or None if it's not intialized)
no_overflow_disconnection: ``bool``
Whether or not cascading failures are computed or not (TRUE = the powerlines above their thermal limits will
not be disconnected). This is initialized based on the attribute
:attr:`grid2op.Parameters.Parameters.NO_OVERFLOW_DISCONNECTION`.
timestep_overflow: ``numpy.ndarray``, dtype: int
Number of consecutive timesteps each powerline has been on overflow.
nb_timestep_overflow_allowed: ``numpy.ndarray``, dtype: int
Number of consecutive timestep each powerline can be on overflow. It is usually read from
:attr:`grid2op.Parameters.Parameters.NB_TIMESTEP_POWERFLOW_ALLOWED`.
hard_overflow_threshold: ``float``
Number of timestep before an :class:`grid2op.BaseAgent.BaseAgent` can reconnet a powerline that has been disconnected
by the environment due to an overflow.
env_dc: ``bool``
Whether the environment computes the powerflow using the DC approximation or not. It is usually read from
:attr:`grid2op.Parameters.Parameters.ENV_DC`.
chronics_handler: :class:`grid2op.ChronicsHandler.ChronicsHandler`
Helper to get the modification of each time step during the episode.
names_chronics_to_backend: ``dict``
Configuration file used to associated the name of the objects in the backend
(both extremities of powerlines, load or production for
example) with the same object in the data (:attr:`Environment.chronics_handler`). The idea is that, usually
data generation comes from a different software that does not take into account the powergrid infrastructure.
Hence, the same "object" can have a different name. This mapping is present to avoid the need to rename
the "object" when providing data. A more detailed description is available at
:func:`grid2op.ChronicsHandler.GridValue.initialize`.
reward_helper: :class:`grid2p.BaseReward.RewardHelper`
Helper that is called to compute the reward at each time step.
action_space: :class:`grid2op.Action.ActionSpace`
Another name for :attr:`Environment.helper_action_player` for gym compatibility.
observation_space: :class:`grid2op.Observation.ObservationSpace`
Another name for :attr:`Environment.helper_observation` for gym compatibility.
reward_range: ``(float, float)``
The range of the reward function
metadata: ``dict``
For gym compatibility, do not use
spec: ``None``
For Gym compatibility, do not use
viewer: ``object``
Used to display the powergrid. Currently not supported.
env_modification: :class:`grid2op.Action.Action`
Representation of the actions of the environment for the modification of the powergrid.
current_reward: ``float``
The reward of the current time step
TODO update with maintenance, hazards etc. see below
# store actions "cooldown"
times_before_line_status_actionable
max_timestep_line_status_deactivated
times_before_topology_actionable
max_timestep_topology_deactivated
time_next_maintenance
duration_next_maintenance
hard_overflow_threshold
time_remaining_before_reconnection
# redispacthing
target_dispatch
actual_dispatch
gen_activeprod_t:
Should be initialized at 0. for "step" to properly recognize it's the first time step of the game
other_rewards: ``dict``
Dictionnary with key being the name (identifier) and value being some RewardHelper. At each time step, all the
values will be computed by the :class:`Environment` and the information about it will be returned in the
"reward" key of the "info" dictionnary of the :func:`Environment.step`.
"""
def __init__(self,
init_grid_path: str,
chronics_handler,
backend,
parameters,
names_chronics_to_backend=None,
actionClass=TopologyAction,
observationClass=CompleteObservation,
rewardClass=FlatReward,
legalActClass=AlwaysLegal,
voltagecontrolerClass=ControlVoltageFromFile,
other_rewards={},
thermal_limit_a=None,
epsilon_poly=1e-2,
tol_poly=1e-6,
opponent_action_class=DontAct,
opponent_class=BaseOpponent,
opponent_init_budget=0):
"""
Initialize the environment. See the descirption of :class:`grid2op.Environment.Environment` for more information.
Parameters
----------
init_grid_path: ``str``
Used to initailize :attr:`Environment.init_grid_path`
chronics_handler
backend
parameters
names_chronics_to_backend
actionClass
observationClass
rewardClass
legalActClass
"""
# TODO documentation!!
_BasicEnv.__init__(self,
parameters=parameters,
thermal_limit_a=thermal_limit_a,
epsilon_poly=epsilon_poly,
tol_poly=tol_poly,
other_rewards=other_rewards)
# the voltage controler
self.voltagecontrolerClass = voltagecontrolerClass
self.voltage_controler = None
# for gym compatibility (initialized below)
self.action_space = None
self.observation_space = None
self.reward_range = None
self.viewer = None
self.metadata = None
self.spec = None
# for opponent (should be defined here) after the initialization of _BasicEnv
self.opponent_action_class = opponent_action_class
self.opponent_class = opponent_class
self.opponent_init_budget = opponent_init_budget
# for plotting
self.init_backend(init_grid_path, chronics_handler, backend,
names_chronics_to_backend, actionClass,
observationClass, rewardClass, legalActClass)
def init_backend(self, init_grid_path, chronics_handler, backend,
names_chronics_to_backend, actionClass, observationClass,
rewardClass, legalActClass):
if not isinstance(rewardClass, type):
raise Grid2OpException(
"Parameter \"rewardClass\" used to build the Environment 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 Grid2OpException(
"Parameter \"rewardClass\" used to build the Environment should derived form the grid2op.BaseReward class, "
"type provided is \"{}\"".format(type(rewardClass)))
self.rewardClass = rewardClass
self.actionClass = actionClass
self.observationClass = observationClass
# backend
self.init_grid_path = os.path.abspath(init_grid_path)
if not isinstance(backend, Backend):
raise Grid2OpException(
"Parameter \"backend\" used to build the Environment should derived form the grid2op.Backend class, "
"type provided is \"{}\"".format(type(backend)))
self.backend = backend
self.backend.load_grid(
self.init_grid_path) # the real powergrid of the environment
self.backend.load_redispacthing_data(
os.path.split(self.init_grid_path)[0])
self.backend.load_grid_layout(os.path.split(self.init_grid_path)[0])
self.backend.assert_grid_correct()
self.init_grid(backend)
self._has_been_initialized(
) # really important to include this piece of code!
if self._thermal_limit_a is None:
self._thermal_limit_a = self.backend.thermal_limit_a
else:
self.backend.set_thermal_limit(self._thermal_limit_a)
*_, tmp = self.backend.generators_info()
# rules of the game
if not isinstance(legalActClass, type):
raise Grid2OpException(
"Parameter \"legalActClass\" used to build the Environment 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 Grid2OpException(
"Parameter \"legalActClass\" used to build the Environment should derived form the "
"grid2op.BaseRules class, type provided is \"{}\"".format(
type(legalActClass)))
self.game_rules = RulesChecker(legalActClass=legalActClass)
self.legalActClass = legalActClass
# action helper
if not isinstance(actionClass, type):
raise Grid2OpException(
"Parameter \"actionClass\" used to build the Environment should be a type (a class) "
"and not an object (an instance of a class). "
"It is currently \"{}\"".format(type(legalActClass)))
if not issubclass(actionClass, BaseAction):
raise Grid2OpException(
"Parameter \"actionClass\" used to build the Environment should derived form the "
"grid2op.BaseAction class, type provided is \"{}\"".format(
type(actionClass)))
if not isinstance(observationClass, type):
raise Grid2OpException(
"Parameter \"actionClass\" used to build the Environment should be a type (a class) "
"and not an object (an instance of a class). "
"It is currently \"{}\"".format(type(legalActClass)))
if not issubclass(observationClass, BaseObservation):
raise Grid2OpException(
"Parameter \"observationClass\" used to build the Environment should derived form the "
"grid2op.BaseObservation class, type provided is \"{}\"".
format(type(observationClass)))
# action affecting the grid that will be made by the agent
self.helper_action_player = ActionSpace(
gridobj=self.backend,
actionClass=actionClass,
legal_action=self.game_rules.legal_action)
# action that affect the grid made by the environment.
self.helper_action_env = ActionSpace(
gridobj=self.backend,
actionClass=CompleteAction,
legal_action=self.game_rules.legal_action)
self.helper_observation = ObservationSpace(
gridobj=self.backend,
observationClass=observationClass,
rewardClass=rewardClass,
env=self)
# handles input data
if not isinstance(chronics_handler, ChronicsHandler):
raise Grid2OpException(
"Parameter \"chronics_handler\" used to build the Environment should derived form the "
"grid2op.ChronicsHandler class, type provided is \"{}\"".
format(type(chronics_handler)))
self.chronics_handler = chronics_handler
self.chronics_handler.initialize(
self.name_load,
self.name_gen,
self.name_line,
self.name_sub,
names_chronics_to_backend=names_chronics_to_backend)
self.names_chronics_to_backend = names_chronics_to_backend
# test to make sure the backend is consistent with the chronics generator
self.chronics_handler.check_validity(self.backend)
# reward function
self.reward_helper = RewardHelper(self.rewardClass)
self.reward_helper.initialize(self)
for k, v in self.other_rewards.items():
v.initialize(self)
# controler for voltage
if not issubclass(self.voltagecontrolerClass, BaseVoltageController):
raise Grid2OpException(
"Parameter \"voltagecontrolClass\" should derive from \"ControlVoltageFromFile\"."
)
self.voltage_controler = self.voltagecontrolerClass(
gridobj=self.backend, controler_backend=self.backend)
# create the opponent
# At least the 3 following attributes should be set before calling _create_opponent
# self.opponent_action_class
# self.opponent_class
# self.opponent_init_budget
self._create_opponent()
# performs one step to load the environment properly (first action need to be taken at first time step after
# first injections given)
self._reset_maintenance()
do_nothing = self.helper_action_env({})
*_, fail_to_start, info = self.step(do_nothing)
if fail_to_start:
raise Grid2OpException(
"Impossible to initialize the powergrid, the powerflow diverge at iteration 0. "
"Available information are: {}".format(info))
# test the backend returns object of the proper size
self.backend.assert_grid_correct_after_powerflow()
# for gym compatibility
self.action_space = self.helper_action_player # this should be an action !!!
self.observation_space = self.helper_observation # this return an observation.
self.reward_range = self.reward_helper.range()
self.viewer = None
self.metadata = {'render.modes': ["human", "rgb_array"]}
self.spec = None
self.current_reward = self.reward_range[0]
self.done = False
# reset everything to be consistent
self._reset_vectors_and_timings()
def _voltage_control(self, agent_action, prod_v_chronics):
"""
Update the environment action "action_env" given a possibly new voltage setpoint for the generators. This
function can be overide for a more complex handling of the voltages.
It mush update (if needed) the voltages of the environment action :attr:`BasicEnv.env_modification`
Parameters
----------
agent_action: :class:`grid2op.Action.Action`
The action performed by the player (or do nothing is player action were not legal or ambiguous)
prod_v_chronics: ``numpy.ndarray`` or ``None``
The voltages that has been specified in the chronics
"""
self.env_modification += self.voltage_controler.fix_voltage(
self.current_obs, agent_action, self.env_modification,
prod_v_chronics)
def set_chunk_size(self, new_chunk_size):
"""
For an efficient data pipeline, it can be usefull to not read all part of the input data
(for example for load_p, prod_p, load_q, prod_v). Grid2Op support the reading of large chronics by "chunk"
of given size.
Reading data in chunk can also reduce the memory footprint, useful in case of multiprocessing environment while
large chronics.
It is critical to set a small chunk_size in case of training machine learning algorithm (reinforcement
learning agent) at the beginning when the agent performs poorly, the software might spend most of its time
loading the data.
**NB** this has no effect if the chronics does not support this feature. TODO see xxx for more information
**NB** The environment need to be **reset** for this to take effect (it won't affect the chronics already
loaded)
Parameters
----------
new_chunk_size: ``int`` or ``None``
The new chunk size (positive integer)
"""
if new_chunk_size is None:
self.chronics_handler.set_chunk_size(new_chunk_size)
return
try:
new_chunk_size = int(new_chunk_size)
except Exception as e:
raise Grid2OpException(
"Impossible to set the chunk size. It should be convertible a integer, and not"
"{}".format(new_chunk_size))
if new_chunk_size <= 0:
raise Grid2OpException(
"Impossible to read less than 1 data at a time. Please make sure \"new_chunk_size\""
"is a positive integer.")
self.chronics_handler.set_chunk_size(new_chunk_size)
def set_id(self, id_):
"""
Set the id that will be used at the next call to :func:`Environment.reset`.
**NB** this has no effect if the chronics does not support this feature. TODO see xxx for more information
**NB** The environment need to be **reset** for this to take effect.
Parameters
----------
id_: ``int``
the id of the chronics used.
Examples
--------
Here an example that will loop 10 times through the same chronics (always using the same injection then):
.. code-block:: python
import grid2op
from grid2op import make
from grid2op.BaseAgent import DoNothingAgent
env = make("case14_redisp") # create an environment
agent = DoNothingAgent(env.action_space) # create an BaseAgent
for i in range(10):
env.set_id(0) # tell the environment you simply want to use the chronics with ID 0
obs = env.reset() # it is necessary to perform a reset
reward = env.reward_range[0]
done = False
while not done:
act = agent.act(obs, reward, done)
obs, reward, done, info = env.step(act)
"""
self.chronics_handler.tell_id(id_ - 1)
def attach_renderer(self, graph_layout=None):
if self.viewer is not None:
return
if graph_layout is not None:
self.viewer = PlotPyGame(observation_space=self.helper_observation,
substation_layout=graph_layout)
self.viewer.reset(self)
else:
raise PlotError(
"No layout are available for the powergrid. Renderer is not possible."
)
def __str__(self):
return '<{} instance>'.format(type(self).__name__)
# TODO be closer to original gym implementation
# if self.spec is None:
# return '<{} instance>'.format(type(self).__name__)
# else:
# return '<{}<{}>>'.format(type(self).__name__, self.spec.id)
def reset_grid(self):
"""
Reset the backend to a clean state by reloading the powergrid from the hard drive. This might takes some time.
If the thermal has been modified, it also modify them into the new backend.
"""
self.backend.load_grid(
self.init_grid_path) # the real powergrid of the environment
self.backend.assert_grid_correct()
if self._thermal_limit_a is not None:
self.backend.set_thermal_limit(self._thermal_limit_a)
# TODO this is super weird!!!!
# self.gen_downtime = self.gen_min_downtime + 1
# self.gen_uptime = self.gen_min_uptime + 1
do_nothing = self.helper_action_env({})
*_, fail_to_start, info = self.step(do_nothing)
if fail_to_start:
raise Grid2OpException(
"Impossible to initialize the powergrid, the powerflow diverge at iteration 0. "
"Available information are: {}".format(info))
# test the backend returns object of the proper size
self.backend.assert_grid_correct_after_powerflow()
def add_text_logger(self, logger=None):
"""
Add a text logger to this :class:`Environment`
Logging is for now an incomplete feature. It will get improved
Parameters
----------
logger:
The logger to use
"""
self.logger = logger
return self
def seed(self, seed=None):
"""
Set the seed of this :class:`Environment` for a better control and to ease reproducible experiments.
This is not supported yet.
Parameters
----------
seed: ``int``
The seed to set.
"""
try:
seed = np.array(seed).astype('int64')
except Exception as e:
raise Grid2OpException(
"Impossible to seed with the seed provided. Make sure it can be converted to a"
"numpy 64 integer.")
# example from gym
# self.np_random, seed = seeding.np_random(seed)
# TODO make that more clean, see example of seeding @ https://github.com/openai/gym/tree/master/gym/utils
self.chronics_handler.seed(seed)
self.helper_observation.seed(seed)
self.helper_action_player.seed(seed)
self.helper_action_env.seed(seed)
return [seed]
def reset(self):
"""
Reset the environment to a clean state.
It will reload the next chronics if any. And reset the grid to a clean state.
This triggers a full reloading of both the chronics (if they are stored as files) and of the powergrid,
to ensure the episode is fully over.
This method should be called only at the end of an episode.
"""
self.chronics_handler.next_chronics()
self.chronics_handler.initialize(
self.backend.name_load,
self.backend.name_gen,
self.backend.name_line,
self.backend.name_sub,
names_chronics_to_backend=self.names_chronics_to_backend)
self.current_obs = None
self.env_modification = None
self._reset_maintenance()
self._reset_redispatching()
self._reset_vectors_and_timings(
) # it need to be done BEFORE to prevent cascading failure when there has been
self.reset_grid()
if self.viewer is not None:
self.viewer.reset(self)
# if True, then it will not disconnect lines above their thermal limits
self._reset_vectors_and_timings(
) # and it needs to be done AFTER to have proper timings at tbe beginning
# TODO add test above: fake a cascading failure, do a reset, check that it can be loaded
# reset the opponent
self.oppSpace.reset()
return self.get_obs()
def render(self, mode='human'):
err_msg = "Impossible to use the renderer, please set it up with \"env.init_renderer(graph_layout)\", " \
"graph_layout being the position of each substation of the powergrid that you must provide"
self.attach_renderer()
if mode == "human":
if self.viewer is not None:
has_quit = self.viewer.render(self.current_obs,
reward=self.current_reward,
timestamp=self.time_stamp,
done=self.done)
if has_quit:
self.close()
exit()
else:
raise Grid2OpException(err_msg)
elif mode == "rgb_array":
if self.viewer is not None:
return np.array(
self.viewer.get_rgb(self.current_obs,
reward=self.current_reward,
timestamp=self.time_stamp,
done=self.done))
else:
raise Grid2OpException(err_msg)
else:
raise Grid2OpException(
"Renderer mode \"{}\" not supported.".format(mode))
def copy(self):
"""
performs a deep copy of the environment
Returns
-------
"""
tmp_backend = self.backend
self.backend = None
res = copy.deepcopy(self)
res.backend = tmp_backend.copy()
if self._thermal_limit_a is not None:
res.backend.set_thermal_limit(self._thermal_limit_a)
self.backend = tmp_backend
return res
def get_kwargs(self):
"""
This function allows to make another Environment with the same parameters as the one that have been used
to make this one.
This is usefull especially in cases where Environment is not pickable (for example if some non pickable c++
code are used) but you still want to make parallel processing using "MultiProcessing" module. In that case,
you can send this dictionnary to each child process, and have each child process make a copy of ``self``
Returns
-------
res: ``dict``
A dictionnary that helps build an environment like ``self``
Examples
--------
It should be used as follow:
.. code-block:: python
import grid2op
from grid2op.Environment import Environment
env = grid2op.make() # create the environment of your choice
copy_of_env = Environment(**env.get_kwargs())
# And you can use this one as you would any other environment.
"""
res = {}
res["init_grid_path"] = self.init_grid_path
res["chronics_handler"] = copy.deepcopy(self.chronics_handler)
res["parameters"] = copy.deepcopy(self.parameters)
res["names_chronics_to_backend"] = copy.deepcopy(
self.names_chronics_to_backend)
res["actionClass"] = self.actionClass
res["observationClass"] = self.observationClass
res["rewardClass"] = self.rewardClass
res["legalActClass"] = self.legalActClass
res["epsilon_poly"] = self._epsilon_poly
res["tol_poly"] = self._tol_poly
res["thermal_limit_a"] = self._thermal_limit_a
res["voltagecontrolerClass"] = self.voltagecontrolerClass
res["other_rewards"] = {
k: v.rewardClass
for k, v in self.other_rewards.items()
}
res["opponent_action_class"] = self.opponent_action_class
res["opponent_class"] = self.opponent_class
res["opponent_init_budget"] = self.opponent_init_budget
return res
def get_params_for_runner(self):
"""
This method is used to initialize a proper :class:`grid2op.Runner.Runner` to use this specific environment.
Examples
--------
It should be used as followed:
.. code-block:: python
import grid2op
from grid2op.Runner import Runner
env = grid2op.make() # create the environment of your choice
agent = DoNothingAgent(env.actoin_space)
# create the proper runner
runner = Runner(**env.get_params_for_runner(), agentClass=DoNothingAgent)
# now you can run
runner.run(nb_episode=1) # run for 1 episode
"""
res = {}
res["init_grid_path"] = self.init_grid_path
res["path_chron"] = self.chronics_handler.path
res["parameters_path"] = self.parameters.to_dict()
res["names_chronics_to_backend"] = self.names_chronics_to_backend
res["actionClass"] = self.actionClass
res["observationClass"] = self.observationClass
res["rewardClass"] = self.rewardClass
res["legalActClass"] = self.legalActClass
res["envClass"] = Environment
res["gridStateclass"] = self.chronics_handler.chronicsClass
res["backendClass"] = type(self.backend) # TODO
res["verbose"] = False
dict_ = copy.deepcopy(self.chronics_handler.kwargs)
if 'path' in dict_:
# path is handled elsewhere
del dict_["path"]
res["gridStateclass_kwargs"] = dict_
res["thermal_limit_a"] = self._thermal_limit_a
res["voltageControlerClass"] = self.voltagecontrolerClass
res["other_rewards"] = {
k: v.rewardClass
for k, v in self.other_rewards.items()
}
res["opponent_action_class"] = self.opponent_action_class
res["opponent_class"] = self.opponent_class
res["opponent_init_budget"] = self.opponent_init_budget
res["grid_layout"] = self.grid_layout
# TODO make a test for that
return res
class BaseVoltageController(RandomObject, ABC):
"""
This class is the most basic controler for the voltages. Basically, what it does is read the voltages from the
chronics.
If the voltages are not on the chronics (missing files), it will not change the voltage setpoints at all.
"""
def __init__(self, gridobj, controler_backend):
"""
Parameters
----------
gridobj: :class:`grid2op.Space.Gridobject`
Structure of the powergrid
envbackend: :class:`grid2op.Backend.Backend`
An instanciated backend to perform some computation on a powergrid, before taking some actions.
"""
RandomObject.__init__(self)
legal_act = AlwaysLegal()
self.action_space = ActionSpace(gridobj=gridobj,
actionClass=VoltageOnlyAction,
legal_action=legal_act)
self.backend = controler_backend.copy()
def attach_layout(self, grid_layout):
self.action_space.attach_layout(grid_layout)
def seed(self, seed):
me_seed = super().seed(seed)
max_int = np.iinfo(dt_int).max
seed_space = self.space_prng.randint(max_int)
space_seed = self.action_space.seed(seed_space)
return me_seed, space_seed
@abstractmethod
def fix_voltage(self, observation, agent_action, env_action,
prod_v_chronics):
"""
This method must be overloaded to change the behaviour of the generator setpoint for time t+1.
This simple class will:
- do nothing if the vector `prod_v_chronics` is None
- set the generator setpoint to the value in prod_v_chronics
Basically, this class is pretty fast, but does nothing interesting, beside looking at the data in files.
More general class can use, to adapt the voltage setpoint:
- `observation` the observation (receive by the agent) at time t
- `agent_action` the action of the agent at time t
- `env_action` the modification of the environment at time t, that will be observed by the agent at time
t+1
- `prod_v_chronics` the new setpoint of the generators present in the data (if any, this can be None)
To help this class, a :class:`grid2op.Backend.Backend` is available and can be used to perform simulation of
potential impact of voltages setpoints.
Parameters
----------
observation: :class:`grid2op.Observation.Observation`
The last observation (at time t)
agent_action: :class:`grid2op.Action.Action`
The action that the agent took
env_action: :class:`grid2op.Action.Action`
The modification that the environment will take.
prod_v_chronics: ``numpy.ndarray``
The next voltage setpoint present in the data (if any) or ``None`` if not.
Returns
-------
res: :class:`grid2op.Action.Action`
The new setpoint, in this case depending only on the prod_v_chronics.
"""
pass