def __init__(self,
backend_instanciated,
completeActionClass,
parameters,
reward_helper,
obsClass,
action_helper,
thermal_limit_a,
legalActClass,
donothing_act,
helper_action_class,
helper_action_env,
other_rewards={}):
BaseEnv.__init__(self,
copy.deepcopy(parameters),
thermal_limit_a,
other_rewards=other_rewards)
self._helper_action_class = helper_action_class
self._reward_helper = reward_helper
self._obsClass = None
self.gen_activeprod_t_init = np.zeros(self.n_gen, dtype=dt_float)
self.gen_activeprod_t_redisp_init = np.zeros(self.n_gen,
dtype=dt_float)
self.times_before_line_status_actionable_init = np.zeros(self.n_line,
dtype=dt_int)
self.times_before_topology_actionable_init = np.zeros(self.n_sub,
dtype=dt_int)
self.time_next_maintenance_init = np.zeros(self.n_line, dtype=dt_int)
self.duration_next_maintenance_init = np.zeros(self.n_line,
dtype=dt_int)
self.target_dispatch_init = np.zeros(self.n_gen, dtype=dt_float)
self.actual_dispatch_init = np.zeros(self.n_gen, dtype=dt_float)
self._init_backend(init_grid_path=None,
chronics_handler=_ObsCH(),
backend=backend_instanciated,
names_chronics_to_backend=None,
actionClass=action_helper.actionClass,
observationClass=obsClass,
rewardClass=None,
legalActClass=legalActClass)
self.no_overflow_disconnection = parameters.NO_OVERFLOW_DISCONNECTION
self._load_p, self._load_q, self._load_v = None, None, None
self._prod_p, self._prod_q, self._prod_v = None, None, None
self._topo_vect = None
# convert line status to -1 / 1 instead of false / true
self._line_status = None
self.is_init = False
self._helper_action_env = helper_action_env
self.env_modification = self._helper_action_env()
self._do_nothing_act = self._helper_action_env()
self._backend_action_set = self._backend_action_class()
# opponent
self.opp_space_state = None
self.opp_state = None
def __init__(self,
init_grid_path: str,
chronics_handler,
backend,
parameters,
name="unknown",
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,
_raw_backend_class=None,
with_forecast=True):
BaseEnv.__init__(self,
parameters=parameters,
thermal_limit_a=thermal_limit_a,
epsilon_poly=epsilon_poly,
tol_poly=tol_poly,
other_rewards=other_rewards,
with_forecast=with_forecast)
if name == "unknown":
warnings.warn(
"It is NOT recommended to create an environment without \"make\" and EVEN LESS "
"to use an environment without a name")
self.name = name
# 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 BaseEnv
self.opponent_action_class = opponent_action_class
self.opponent_class = opponent_class
self.opponent_init_budget = opponent_init_budget
if _raw_backend_class is None:
self._raw_backend_class = type(backend)
else:
_raw_backend_class = _raw_backend_class
# for plotting
self.init_backend(init_grid_path, chronics_handler, backend,
names_chronics_to_backend, actionClass,
observationClass, rewardClass, legalActClass)
def __init__(self,
backend_instanciated,
completeActionClass,
parameters,
reward_helper,
obsClass,
action_helper,
thermal_limit_a,
legalActClass,
donothing_act,
helper_action_class,
helper_action_env,
other_rewards={}):
BaseEnv.__init__(self,
parameters,
thermal_limit_a,
other_rewards=other_rewards)
self.helper_action_class = helper_action_class
self.reward_helper = reward_helper
self.obsClass = None
# self._action = None
self.CompleteActionClass = completeActionClass
self.init_backend(init_grid_path=None,
chronics_handler=_ObsCH(),
backend=backend_instanciated,
names_chronics_to_backend=None,
actionClass=action_helper.actionClass,
observationClass=obsClass,
rewardClass=None,
legalActClass=legalActClass)
self.no_overflow_disconnection = parameters.NO_OVERFLOW_DISCONNECTION
self._load_p, self._load_q, self._load_v = None, None, None
self._prod_p, self._prod_q, self._prod_v = None, None, None
self._topo_vect = None
# convert line status to -1 / 1 instead of false / true
self._line_status = None
self.is_init = False
self.helper_action_env = helper_action_env
self.env_modification = self.helper_action_env()
self._do_nothing_act = self.helper_action_env()
self._backend_action_set = self._backend_action_class()
# opponent
self.opp_space_state = None
self.opp_state = None
def __init__(self,
init_grid_path: str,
chronics_handler,
backend,
parameters,
name="unknown",
names_chronics_to_backend=None,
actionClass=TopologyAction,
observationClass=CompleteObservation,
rewardClass=FlatReward,
legalActClass=AlwaysLegal,
voltagecontrolerClass=ControlVoltageFromFile,
other_rewards={},
thermal_limit_a=None,
with_forecast=True,
epsilon_poly=1e-4, # precision of the redispatching algorithm we don't recommend to go above 1e-4
tol_poly=1e-2, # i need to compute a redispatching if the actual values are "more than tol_poly" the values they should be
opponent_action_class=DontAct,
opponent_class=BaseOpponent,
opponent_init_budget=0.,
opponent_budget_per_ts=0.,
opponent_budget_class=NeverAttackBudget,
opponent_attack_duration=0,
opponent_attack_cooldown=99999,
kwargs_opponent={},
_raw_backend_class=None
):
BaseEnv.__init__(self,
parameters=parameters,
thermal_limit_a=thermal_limit_a,
epsilon_poly=epsilon_poly,
tol_poly=tol_poly,
other_rewards=other_rewards,
with_forecast=with_forecast,
voltagecontrolerClass=voltagecontrolerClass,
opponent_action_class=opponent_action_class,
opponent_class=opponent_class,
opponent_budget_class=opponent_budget_class,
opponent_init_budget=opponent_init_budget,
opponent_budget_per_ts=opponent_budget_per_ts,
opponent_attack_duration=opponent_attack_duration,
opponent_attack_cooldown=opponent_attack_cooldown,
kwargs_opponent=kwargs_opponent)
if name == "unknown":
warnings.warn("It is NOT recommended to create an environment without \"make\" and EVEN LESS "
"to use an environment without a name")
self.name = name
# 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
if _raw_backend_class is None:
self._raw_backend_class = type(backend)
else:
self._raw_backend_class = _raw_backend_class
# for plotting
self._init_backend(init_grid_path, chronics_handler, backend,
names_chronics_to_backend, actionClass, observationClass,
rewardClass, legalActClass)
def __init__(
self,
init_grid_path,
backend_instanciated,
parameters,
reward_helper,
obsClass,
action_helper,
thermal_limit_a,
legalActClass,
helper_action_class,
helper_action_env,
epsilon_poly,
tol_poly,
max_episode_duration,
other_rewards={},
has_attention_budget=False,
attention_budget_cls=LinearAttentionBudget,
kwargs_attention_budget={},
):
BaseEnv.__init__(self,
init_grid_path,
copy.deepcopy(parameters),
thermal_limit_a,
other_rewards=other_rewards,
epsilon_poly=epsilon_poly,
tol_poly=tol_poly,
has_attention_budget=has_attention_budget,
attention_budget_cls=attention_budget_cls,
kwargs_attention_budget=kwargs_attention_budget)
self._helper_action_class = helper_action_class
self._reward_helper = reward_helper
self._obsClass = None
self._obsClass = obsClass.init_grid(type(backend_instanciated))
self.gen_activeprod_t_init = np.zeros(self.n_gen, dtype=dt_float)
self.gen_activeprod_t_redisp_init = np.zeros(self.n_gen,
dtype=dt_float)
self.times_before_line_status_actionable_init = np.zeros(self.n_line,
dtype=dt_int)
self.times_before_topology_actionable_init = np.zeros(self.n_sub,
dtype=dt_int)
self.time_next_maintenance_init = np.zeros(self.n_line, dtype=dt_int)
self.duration_next_maintenance_init = np.zeros(self.n_line,
dtype=dt_int)
self.target_dispatch_init = np.zeros(self.n_gen, dtype=dt_float)
self.actual_dispatch_init = np.zeros(self.n_gen, dtype=dt_float)
self._init_backend(chronics_handler=_ObsCH(),
backend=backend_instanciated,
names_chronics_to_backend=None,
actionClass=action_helper.actionClass,
observationClass=self._obsClass,
rewardClass=None,
legalActClass=legalActClass)
self.no_overflow_disconnection = parameters.NO_OVERFLOW_DISCONNECTION
self._load_p, self._load_q, self._load_v = None, None, None
self._prod_p, self._prod_q, self._prod_v = None, None, None
self._topo_vect = None
# convert line status to -1 / 1 instead of false / true
self._line_status = None
self.is_init = False
self._helper_action_env = helper_action_env
self.env_modification = self._helper_action_env()
self._do_nothing_act = self._helper_action_env()
self._backend_action_set = self._backend_action_class()
# opponent
self.opp_space_state = None
self.opp_state = None
# storage
self._storage_current_charge_init = None
self._storage_previous_charge_init = None
self._action_storage_init = None
self._amount_storage_init = None
self._amount_storage_prev_init = None
self._storage_power_init = None
# storage unit
self._storage_current_charge_init = np.zeros(self.n_storage,
dtype=dt_float)
self._storage_previous_charge_init = np.zeros(self.n_storage,
dtype=dt_float)
self._action_storage_init = np.zeros(self.n_storage, dtype=dt_float)
self._storage_power_init = np.zeros(self.n_storage, dtype=dt_float)
self._amount_storage_init = 0.
self._amount_storage_prev_init = 0.
# curtailment
self._limit_curtailment_init = np.zeros(self.n_gen, dtype=dt_float)
self._gen_before_curtailment_init = np.zeros(self.n_gen,
dtype=dt_float)
self._sum_curtailment_mw_init = 0.
self._sum_curtailment_mw_prev_init = 0.
# step count
self._nb_time_step_init = 0
# alarm / attention budget
self._attention_budget_state_init = None
self._disc_lines = np.zeros(shape=self.n_line, dtype=dt_int) - 1
self._max_episode_duration = max_episode_duration
def __init__(
self,
init_grid_path,
backend_instanciated,
parameters,
reward_helper,
obsClass, # not initialized :-/
action_helper,
thermal_limit_a,
legalActClass,
helper_action_class,
helper_action_env,
epsilon_poly,
tol_poly,
max_episode_duration,
other_rewards={},
has_attention_budget=False,
attention_budget_cls=LinearAttentionBudget,
kwargs_attention_budget={},
_complete_action_cls=None,
):
BaseEnv.__init__(self,
init_grid_path,
copy.deepcopy(parameters),
thermal_limit_a,
other_rewards=other_rewards,
epsilon_poly=epsilon_poly,
tol_poly=tol_poly,
has_attention_budget=has_attention_budget,
attention_budget_cls=attention_budget_cls,
kwargs_attention_budget=kwargs_attention_budget)
self._reward_helper = reward_helper
self._helper_action_class = helper_action_class
# initialize the observation space
self._obsClass = None
self.gen_activeprod_t_init = np.zeros(self.n_gen, dtype=dt_float)
self.gen_activeprod_t_redisp_init = np.zeros(self.n_gen,
dtype=dt_float)
self.times_before_line_status_actionable_init = np.zeros(self.n_line,
dtype=dt_int)
self.times_before_topology_actionable_init = np.zeros(self.n_sub,
dtype=dt_int)
self.time_next_maintenance_init = np.zeros(self.n_line, dtype=dt_int)
self.duration_next_maintenance_init = np.zeros(self.n_line,
dtype=dt_int)
self.target_dispatch_init = np.zeros(self.n_gen, dtype=dt_float)
self.actual_dispatch_init = np.zeros(self.n_gen, dtype=dt_float)
# line status (inherited from BaseEnv)
self._line_status = np.full(self.n_line,
dtype=dt_bool,
fill_value=True)
# line status (for this usage)
self._line_status_me = np.ones(
shape=self.n_line,
dtype=dt_int) # this is "line status" but encode in +1 / -1
self._line_status_orig = np.ones(shape=self.n_line, dtype=dt_int)
self._init_backend(chronics_handler=_ObsCH(),
backend=backend_instanciated,
names_chronics_to_backend=None,
actionClass=action_helper.actionClass,
observationClass=obsClass,
rewardClass=None,
legalActClass=legalActClass)
####
# to be able to save and import (using env.generate_classes) correctly
self._actionClass = action_helper.subtype
self._observationClass = _complete_action_cls # not used anyway
self._complete_action_cls = _complete_action_cls
self._action_space = action_helper # obs env and env share the same action space
self._observation_space = action_helper # not used here, so it's definitely a hack !
####
self.no_overflow_disconnection = parameters.NO_OVERFLOW_DISCONNECTION
self._load_p, self._load_q, self._load_v = None, None, None
self._prod_p, self._prod_q, self._prod_v = None, None, None
self._topo_vect = None
# other stuff
self.is_init = False
self._helper_action_env = helper_action_env
self.env_modification = self._helper_action_env()
self._do_nothing_act = self._helper_action_env()
self._backend_action_set = self._backend_action_class()
# opponent
self.opp_space_state = None
self.opp_state = None
# storage
self._storage_current_charge_init = None
self._storage_previous_charge_init = None
self._action_storage_init = None
self._amount_storage_init = None
self._amount_storage_prev_init = None
self._storage_power_init = None
# storage unit
self._storage_current_charge_init = np.zeros(self.n_storage,
dtype=dt_float)
self._storage_previous_charge_init = np.zeros(self.n_storage,
dtype=dt_float)
self._action_storage_init = np.zeros(self.n_storage, dtype=dt_float)
self._storage_power_init = np.zeros(self.n_storage, dtype=dt_float)
self._amount_storage_init = 0.
self._amount_storage_prev_init = 0.
# curtailment
self._limit_curtailment_init = np.zeros(self.n_gen, dtype=dt_float)
self._gen_before_curtailment_init = np.zeros(self.n_gen,
dtype=dt_float)
self._sum_curtailment_mw_init = 0.
self._sum_curtailment_mw_prev_init = 0.
# step count
self._nb_time_step_init = 0
# alarm / attention budget
self._attention_budget_state_init = None
self._disc_lines = np.zeros(shape=self.n_line, dtype=dt_int) - 1
self._max_episode_duration = max_episode_duration