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.Reward.Reward`
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.GameRules`
The rules of the game (define which actions are legal and which are not)
helper_action_player: :class:`grid2op.Action.HelperAction`
Helper used to manipulate more easily the actions given to / provided by the :class:`grid2op.Agent` (player)
helper_action_env: :class:`grid2op.Action.HelperAction`
Helper used to manipulate more easily the actions given to / provided by the environment to the backend.
helper_observation: :class:`grid2op.Observation.ObservationHelper`
Helper used to generate the observation that will be given to the :class:`grid2op.Agent`
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.Agent.Agent` 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.Reward.RewardHelper`
Helper that is called to compute the reward at each time step.
action_space: :class:`grid2op.Action.HelperAction`
Another name for :attr:`Environment.helper_action_player` for gym compatibility.
observation_space: :class:`grid2op.Observation.ObservationHelper`
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
"""
def __init__(self,
init_grid_path: str,
chronics_handler,
backend,
parameters,
names_chronics_to_backend=None,
actionClass=TopologyAction,
observationClass=CompleteObservation,
rewardClass=FlatReward,
legalActClass=AllwaysLegal,
voltagecontrolerClass=ControlVoltageFromFile,
thermal_limit_a=None,
epsilon_poly=1e-2,
tol_poly=1e-6):
"""
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)
# 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 plotting
self.graph_layout = None
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, Reward):
raise Grid2OpException(
"Parameter \"rewardClass\" used to build the Environment should derived form the grid2op.Reward 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.assert_grid_correct()
self.init_grid(backend)
self._has_been_initialized()
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, LegalAction):
raise Grid2OpException(
"Parameter \"legalActClass\" used to build the Environment should derived form the "
"grid2op.LegalAction class, type provided is \"{}\"".format(
type(legalActClass)))
self.game_rules = GameRules(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, Action):
raise Grid2OpException(
"Parameter \"actionClass\" used to build the Environment should derived form the "
"grid2op.Action 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, Observation):
raise Grid2OpException(
"Parameter \"observationClass\" used to build the Environment should derived form the "
"grid2op.Observation class, type provided is \"{}\"".format(
type(observationClass)))
# action affecting the grid that will be made by the agent
self.helper_action_player = HelperAction(
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 = HelperAction(
gridobj=self.backend,
actionClass=Action,
legal_action=self.game_rules.legal_action)
self.helper_observation = ObservationHelper(
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(rewardClass=rewardClass)
self.reward_helper.initialize(self)
# controler for voltage
if not issubclass(self.voltagecontrolerClass, ControlVoltageFromFile):
raise Grid2OpException(
"Parameter \"voltagecontrolClass\" should derive from \"ControlVoltageFromFile\"."
)
self.voltage_controler = self.voltagecontrolerClass(
gridobj=self.backend, controler_backend=self.backend)
# 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, _ = self.step(do_nothing)
if fail_to_start:
raise Grid2OpException(
"Impossible to initialize the powergrid, the powerflow diverge at iteration 0."
)
# 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
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.Agent import DoNothingAgent
env = make("case14_redisp") # create an environment
agent = DoNothingAgent(env.action_space) # create an Agent
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
graph_layout = self.graph_layout if graph_layout is None else graph_layout
if graph_layout is not None:
self.viewer = Renderer(graph_layout,
observation_space=self.helper_observation)
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)
do_nothing = self.helper_action_env({})
self.step(do_nothing)
# 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._reset_maintenance()
self._reset_redispatching()
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()
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
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
# TODO make a test for that
return res
class Environment:
"""
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.Reward.Reward`
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.GameRules`
The rules of the game (define which actions are legal and which are not)
helper_action_player: :class:`grid2op.Action.HelperAction`
Helper used to manipulate more easily the actions given to / provided by the :class:`grid2op.Agent` (player)
helper_action_env: :class:`grid2op.Action.HelperAction`
Helper used to manipulate more easily the actions given to / provided by the environment to the backend.
helper_observation: :class:`grid2op.Observation.ObservationHelper`
Helper used to generate the observation that will be given to the :class:`grid2op.Agent`
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.Agent.Agent` 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.Reward.RewardHelper`
Helper that is called to compute the reward at each time step.
action_space: :class:`grid2op.Action.HelperAction`
Another name for :attr:`Environment.helper_action_player` for gym compatibility.
observation_space: :class:`grid2op.Observation.ObservationHelper`
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.
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
"""
def __init__(self,
init_grid_path: str,
chronics_handler,
backend,
parameters,
names_chronics_to_backend=None,
actionClass=TopologyAction,
observationClass=CompleteObservation,
rewardClass=FlatReward,
legalActClass=AllwaysLegal):
"""
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!!
# some timers
self._time_apply_act = 0
self._time_powerflow = 0
self._time_extract_obs = 0
# define logger
self.logger = None
# and calendar data
self.time_stamp = None
self.nb_time_step = 0
# specific to power system
if not isinstance(parameters, Parameters):
raise Grid2OpException(
"Parameter \"parameters\" used to build the Environment should derived form the "
"grid2op.Parameters class, type provided is \"{}\"".format(
type(parameters)))
self.parameters = parameters
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, Reward):
raise Grid2OpException(
"Parameter \"rewardClass\" used to build the Environment should derived form the grid2op.Reward class, "
"type provided is \"{}\"".format(type(rewardClass)))
self.rewardClass = rewardClass
# 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.assert_grid_correct()
*_, 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, LegalAction):
raise Grid2OpException(
"Parameter \"legalActClass\" used to build the Environment should derived form the "
"grid2op.LegalAction class, type provided is \"{}\"".format(
type(legalActClass)))
self.game_rules = GameRules(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, Action):
raise Grid2OpException(
"Parameter \"actionClass\" used to build the Environment should derived form the "
"grid2op.Action 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, Observation):
raise Grid2OpException(
"Parameter \"observationClass\" used to build the Environment should derived form the "
"grid2op.Observation class, type provided is \"{}\"".format(
type(observationClass)))
# action affecting the _grid that will be made by the agent
self.helper_action_player = HelperAction(gridobj=self.backend,
actionClass=actionClass,
game_rules=self.game_rules)
# action that affect the _grid made by the environment.
self.helper_action_env = HelperAction(gridobj=self.backend,
actionClass=Action,
game_rules=self.game_rules)
self.helper_observation = ObservationHelper(
gridobj=self.backend,
observationClass=observationClass,
rewardClass=rewardClass,
env=self)
# observation
self.current_obs = None
# type of power flow to play
# if True, then it will not disconnect lines above their thermal limits
self.no_overflow_disconnection = self.parameters.NO_OVERFLOW_DISCONNECTION
self.timestep_overflow = np.zeros(shape=(self.backend.n_line, ),
dtype=np.int)
self.nb_timestep_overflow_allowed = np.full(
shape=(self.backend.n_line, ),
fill_value=self.parameters.NB_TIMESTEP_POWERFLOW_ALLOWED)
# store actions "cooldown"
self.times_before_line_status_actionable = np.zeros(
shape=(self.backend.n_line, ), dtype=np.int)
self.max_timestep_line_status_deactivated = self.parameters.NB_TIMESTEP_LINE_STATUS_REMODIF
self.times_before_topology_actionable = np.zeros(
shape=(self.backend.n_sub, ), dtype=np.int)
self.max_timestep_topology_deactivated = self.parameters.NB_TIMESTEP_TOPOLOGY_REMODIF
# for maintenance operation
self.time_next_maintenance = np.zeros(shape=(self.backend.n_line, ),
dtype=np.int) - 1
self.duration_next_maintenance = np.zeros(
shape=(self.backend.n_line, ), dtype=np.int)
# hazard (not used outside of this class, information is given in `time_remaining_before_line_reconnection`
self._hazard_duration = np.zeros(shape=(self.backend.n_line, ),
dtype=np.int)
# hard overflow part
self.hard_overflow_threshold = self.parameters.HARD_OVERFLOW_THRESHOLD
self.time_remaining_before_line_reconnection = np.full(
shape=(self.backend.n_line, ), fill_value=0, dtype=np.int)
self.env_dc = self.parameters.ENV_DC
# 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.backend.name_load,
self.backend.name_gen,
self.backend.name_line,
self.backend.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)
# store environment modifications
self._injection = None
self._maintenance = None
self._hazards = None
self.env_modification = None
# reward
self.reward_helper = RewardHelper(rewardClass=rewardClass)
self.reward_helper.initialize(self)
# 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, _ = self.step(do_nothing)
if fail_to_start:
raise Grid2OpException(
"Impossible to initialize the powergrid, the powerflow diverge at iteration 0."
)
# 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': []}
self.spec = None
self._reset_vectors_and_timings()
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 __enter__(self):
"""
Support *with-statement* for the environment.
Examples
--------
>>> import grid2op
>>> import grid2op.Agent
>>> with grid2op.make() as env:
>>> agent = grid2op.Agent.DoNothingAgent(env.action_space)
>>> act = env.action_space()
>>> obs, r, done, info = env.step(act)
>>> act = agent.act(obs, r, info)
>>> obs, r, done, info = env.step(act)
"""
return self
def __exit__(self, *args):
"""
Support *with-statement* for the environment.
"""
self.close()
# propagate exception
return False
def _reset_maintenance(self):
self.time_next_maintenance = np.zeros(shape=(self.backend.n_line, ),
dtype=np.int) - 1
self.duration_next_maintenance = np.zeros(
shape=(self.backend.n_line, ), dtype=np.int)
self.time_remaining_before_reconnection = np.full(
shape=(self.backend.n_line, ), fill_value=0, dtype=np.int)
def reset_grid(self):
"""
Reset the backend to a clean state by reloading the powergrid from the hard drive. This might takes some time.
"""
self.backend.load_grid(
self.init_grid_path) # the real powergrid of the environment
self.backend.assert_grid_correct()
do_nothing = self.helper_action_env({})
self.step(do_nothing)
# 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
----------
seed:
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.
"""
# 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)
return [seed]
def _update_actions(self):
"""
Retrieve the actions to perform the update of the underlying powergrid represented by
the :class:`grid2op.Backend`in the next time step.
A call to this function will also read the next state of :attr:`chronics_handler`, so it must be called only
once per time step.
Returns
--------
res: :class:`grid2op.Action.Action`
The action representing the modification of the powergrid induced by the Backend.
"""
timestamp, tmp, maintenance_time, maintenance_duration, hazard_duration = self.chronics_handler.next_time_step(
)
if "injection" in tmp:
self._injection = tmp["injection"]
else:
self._injection = None
if 'maintenance' in tmp:
self._maintenance = tmp['maintenance']
else:
self._maintenance = None
if "hazards" in tmp:
self._hazards = tmp["hazards"]
else:
self._hazards = None
self.time_stamp = timestamp
self.duration_next_maintenance = maintenance_duration
self.time_next_maintenance = maintenance_time
self._hazard_duration = hazard_duration
return self.helper_action_env({
"injection": self._injection,
"maintenance": self._maintenance,
"hazards": self._hazards
})
def get_obs(self):
"""
Return the observations of the current environment made by the :class:`grid2op.Agent.Agent`.
Returns
-------
res: :class:`grid2op.Observation.Observation`
The current Observation given to the :class:`grid2op.Agent.Agent` / bot / controler.
"""
res = self.helper_observation(env=self)
return res
def _get_reward(self, action, has_error, is_done, is_illegal,
is_ambiguous):
return self.reward_helper(action, self, has_error, is_done, is_illegal,
is_ambiguous)
def _is_done(self, has_error, is_done):
no_more_data = self.chronics_handler.done()
return has_error or is_done or no_more_data
def _update_time_reconnection_hazards_maintenance(self):
"""
This supposes that :attr:`Environment.time_remaining_before_line_reconnection` is already updated
with the cascading failure, soft overflow and hard overflow.
It also supposes that :func:`Environment._update_actions` has been called, so that the vectors
:attr:`Environment.duration_next_maintenance`, :attr:`Environment.time_next_maintenance` and
:attr:`Environment._hazard_duration` are updated with the most recent values.
Finally the Environment supposes that this method is called before calling :func:`Environment.get_obs`
This function integrates the hazards and maintenance in the
:attr:`Environment.time_remaining_before_line_reconnection` vector.
For example, if a powerline `i` has no problem
of overflow, but is affected by a hazard, :attr:`Environment.time_remaining_before_line_reconnection`
should be updated with the duration of this hazard (stored in one of the three vector mentionned in the
above paragraph)
For this Environment, we suppose that the maximum of the 3 values are taken into account. The reality would
be more complicated.
Returns
-------
"""
self.time_remaining_before_line_reconnection = np.maximum(
self.time_remaining_before_line_reconnection,
self.duration_next_maintenance)
self.time_remaining_before_line_reconnection = np.maximum(
self.time_remaining_before_line_reconnection,
self._hazard_duration)
def step(self, action):
"""
Run one timestep of the environment's dynamics. When end of
episode is reached, you are responsible for calling `reset()`
to reset this environment's state.
Accepts an action and returns a tuple (observation, reward, done, info).
If the :class:`grid2op.Action.Action` is illegal or ambiguous, the step is performed, but the action is
replaced with a "do nothing" action.
Parameters
----------
action: :class:`grid2op.Action.Action`
an action provided by the agent that is applied on the underlying through the backend.
Returns
-------
observation: :class:`grid2op.Observation.Observation`
agent's observation of the current environment
reward: ``float``
amount of reward returned after previous action
done: ``bool``
whether the episode has ended, in which case further step() calls will return undefined results
info: ``dict``
contains auxiliary diagnostic information (helpful for debugging, and sometimes learning). It is a
dicitonnary with keys:
- "disc_lines": a numpy array (or ``None``) saying, for each powerline if it has been disconnected
due to overflow
- "is_illegal" (``bool``) whether the action given as input was illegal
- "is_ambiguous" (``bool``) whether the action given as input was ambiguous.
"""
has_error = True
is_done = False
disc_lines = None
is_illegal = False
is_ambiguous = False
try:
beg_ = time.time()
is_illegal = not self.game_rules(action=action, env=self)
if is_illegal:
# action is replace by do nothing
action = self.helper_action_player({})
try:
self.backend.apply_action(action)
except AmbiguousAction:
# action has not been implemented on the powergrid because it's ambiguous, it's equivalent to
# "do nothing"
is_ambiguous = True
self.env_modification = self._update_actions()
self.backend.apply_action(self.env_modification)
self._time_apply_act += time.time() - beg_
self.nb_time_step += 1
try:
# compute the next _grid state
beg_ = time.time()
disc_lines, infos = self.backend.next_grid_state(
env=self, is_dc=self.env_dc)
self._time_powerflow += time.time() - beg_
beg_ = time.time()
self.backend.update_thermal_limit(
self) # update the thermal limit, for DLR for example
overflow_lines = self.backend.get_line_overflow()
# one timestep passed, i can maybe reconnect some lines
self.time_remaining_before_line_reconnection[
self.time_remaining_before_line_reconnection > 0] -= 1
# update the vector for lines that have been disconnected
self.time_remaining_before_line_reconnection[disc_lines] = int(
self.parameters.NB_TIMESTEP_RECONNECTION)
self._update_time_reconnection_hazards_maintenance()
# for the powerline that are on overflow, increase this time step
self.timestep_overflow[overflow_lines] += 1
# set to 0 the number of timestep for lines that are not on overflow
self.timestep_overflow[~overflow_lines] = 0
# build the topological action "cooldown"
aff_lines, aff_subs = action.get_topological_impact()
if self.max_timestep_line_status_deactivated > 0:
# this is a feature I want to consider in the parameters
self.times_before_line_status_actionable[
self.times_before_line_status_actionable > 0] -= 1
self.times_before_line_status_actionable[
aff_lines] = self.max_timestep_line_status_deactivated
if self.max_timestep_topology_deactivated > 0:
# this is a feature I want to consider in the parameters
self.times_before_topology_actionable[
self.times_before_topology_actionable > 0] -= 1
self.times_before_topology_actionable[
aff_subs] = self.max_timestep_topology_deactivated
# build the observation
self.current_obs = self.get_obs()
self._time_extract_obs += time.time() - beg_
has_error = False
except Grid2OpException as e:
if self.logger is not None:
self.logger.error(
"Impossible to compute next _grid state with error \"{}\""
.format(e))
except StopIteration:
# episode is over
is_done = True
infos = {
"disc_lines": disc_lines,
"is_illegal": is_illegal,
"is_ambiguous": is_ambiguous
}
return self.current_obs, self._get_reward(action, has_error, is_done, is_illegal, is_ambiguous),\
self._is_done(has_error, is_done),\
infos
def _reset_vectors_and_timings(self):
"""
Maintenance are not reset, otherwise the data are not read properly (skip the first time step)
Returns
-------
"""
self.no_overflow_disconnection = self.parameters.NO_OVERFLOW_DISCONNECTION
self.timestep_overflow = np.zeros(shape=(self.backend.n_line, ),
dtype=np.int)
self.nb_timestep_overflow_allowed = np.full(
shape=(self.backend.n_line, ),
fill_value=self.parameters.NB_TIMESTEP_POWERFLOW_ALLOWED)
self.nb_time_step = 0
self.hard_overflow_threshold = self.parameters.HARD_OVERFLOW_THRESHOLD
self.env_dc = self.parameters.ENV_DC
self.times_before_line_status_actionable = np.zeros(
shape=(self.backend.n_line, ), dtype=np.int)
self.max_timestep_line_status_deactivated = self.parameters.NB_TIMESTEP_LINE_STATUS_REMODIF
self.times_before_topology_actionable = np.zeros(
shape=(self.backend.n_sub, ), dtype=np.int)
self.max_timestep_topology_deactivated = self.parameters.NB_TIMESTEP_TOPOLOGY_REMODIF
self._time_apply_act = 0
self._time_powerflow = 0
self._time_extract_obs = 0
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._reset_maintenance()
self.reset_grid()
# if True, then it will not disconnect lines above their thermal limits
self._reset_vectors_and_timings()
return self.get_obs()
def render(self, mode='human'):
# TODO here, and reuse pypownet
pass
def close(self):
# todo there might be some side effect
if self.viewer:
self.viewer.close()
self.viewer = None
self.backend.close()
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, Reward):
raise Grid2OpException(
"Parameter \"rewardClass\" used to build the Environment should derived form the grid2op.Reward 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.assert_grid_correct()
self.init_grid(backend)
self._has_been_initialized()
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, LegalAction):
raise Grid2OpException(
"Parameter \"legalActClass\" used to build the Environment should derived form the "
"grid2op.LegalAction class, type provided is \"{}\"".format(
type(legalActClass)))
self.game_rules = GameRules(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, Action):
raise Grid2OpException(
"Parameter \"actionClass\" used to build the Environment should derived form the "
"grid2op.Action 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, Observation):
raise Grid2OpException(
"Parameter \"observationClass\" used to build the Environment should derived form the "
"grid2op.Observation class, type provided is \"{}\"".format(
type(observationClass)))
# action affecting the grid that will be made by the agent
self.helper_action_player = HelperAction(
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 = HelperAction(
gridobj=self.backend,
actionClass=Action,
legal_action=self.game_rules.legal_action)
self.helper_observation = ObservationHelper(
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(rewardClass=rewardClass)
self.reward_helper.initialize(self)
# controler for voltage
if not issubclass(self.voltagecontrolerClass, ControlVoltageFromFile):
raise Grid2OpException(
"Parameter \"voltagecontrolClass\" should derive from \"ControlVoltageFromFile\"."
)
self.voltage_controler = self.voltagecontrolerClass(
gridobj=self.backend, controler_backend=self.backend)
# 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, _ = self.step(do_nothing)
if fail_to_start:
raise Grid2OpException(
"Impossible to initialize the powergrid, the powerflow diverge at iteration 0."
)
# 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
self._reset_vectors_and_timings()
def __init__(self,
init_grid_path: str,
chronics_handler,
backend,
parameters,
names_chronics_to_backend=None,
actionClass=TopologyAction,
observationClass=CompleteObservation,
rewardClass=FlatReward,
legalActClass=AllwaysLegal):
"""
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!!
# some timers
self._time_apply_act = 0
self._time_powerflow = 0
self._time_extract_obs = 0
# define logger
self.logger = None
# and calendar data
self.time_stamp = None
self.nb_time_step = 0
# specific to power system
if not isinstance(parameters, Parameters):
raise Grid2OpException(
"Parameter \"parameters\" used to build the Environment should derived form the "
"grid2op.Parameters class, type provided is \"{}\"".format(
type(parameters)))
self.parameters = parameters
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, Reward):
raise Grid2OpException(
"Parameter \"rewardClass\" used to build the Environment should derived form the grid2op.Reward class, "
"type provided is \"{}\"".format(type(rewardClass)))
self.rewardClass = rewardClass
# 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.assert_grid_correct()
*_, 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, LegalAction):
raise Grid2OpException(
"Parameter \"legalActClass\" used to build the Environment should derived form the "
"grid2op.LegalAction class, type provided is \"{}\"".format(
type(legalActClass)))
self.game_rules = GameRules(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, Action):
raise Grid2OpException(
"Parameter \"actionClass\" used to build the Environment should derived form the "
"grid2op.Action 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, Observation):
raise Grid2OpException(
"Parameter \"observationClass\" used to build the Environment should derived form the "
"grid2op.Observation class, type provided is \"{}\"".format(
type(observationClass)))
# action affecting the _grid that will be made by the agent
self.helper_action_player = HelperAction(gridobj=self.backend,
actionClass=actionClass,
game_rules=self.game_rules)
# action that affect the _grid made by the environment.
self.helper_action_env = HelperAction(gridobj=self.backend,
actionClass=Action,
game_rules=self.game_rules)
self.helper_observation = ObservationHelper(
gridobj=self.backend,
observationClass=observationClass,
rewardClass=rewardClass,
env=self)
# observation
self.current_obs = None
# type of power flow to play
# if True, then it will not disconnect lines above their thermal limits
self.no_overflow_disconnection = self.parameters.NO_OVERFLOW_DISCONNECTION
self.timestep_overflow = np.zeros(shape=(self.backend.n_line, ),
dtype=np.int)
self.nb_timestep_overflow_allowed = np.full(
shape=(self.backend.n_line, ),
fill_value=self.parameters.NB_TIMESTEP_POWERFLOW_ALLOWED)
# store actions "cooldown"
self.times_before_line_status_actionable = np.zeros(
shape=(self.backend.n_line, ), dtype=np.int)
self.max_timestep_line_status_deactivated = self.parameters.NB_TIMESTEP_LINE_STATUS_REMODIF
self.times_before_topology_actionable = np.zeros(
shape=(self.backend.n_sub, ), dtype=np.int)
self.max_timestep_topology_deactivated = self.parameters.NB_TIMESTEP_TOPOLOGY_REMODIF
# for maintenance operation
self.time_next_maintenance = np.zeros(shape=(self.backend.n_line, ),
dtype=np.int) - 1
self.duration_next_maintenance = np.zeros(
shape=(self.backend.n_line, ), dtype=np.int)
# hazard (not used outside of this class, information is given in `time_remaining_before_line_reconnection`
self._hazard_duration = np.zeros(shape=(self.backend.n_line, ),
dtype=np.int)
# hard overflow part
self.hard_overflow_threshold = self.parameters.HARD_OVERFLOW_THRESHOLD
self.time_remaining_before_line_reconnection = np.full(
shape=(self.backend.n_line, ), fill_value=0, dtype=np.int)
self.env_dc = self.parameters.ENV_DC
# 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.backend.name_load,
self.backend.name_gen,
self.backend.name_line,
self.backend.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)
# store environment modifications
self._injection = None
self._maintenance = None
self._hazards = None
self.env_modification = None
# reward
self.reward_helper = RewardHelper(rewardClass=rewardClass)
self.reward_helper.initialize(self)
# 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, _ = self.step(do_nothing)
if fail_to_start:
raise Grid2OpException(
"Impossible to initialize the powergrid, the powerflow diverge at iteration 0."
)
# 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': []}
self.spec = None
self._reset_vectors_and_timings()