def test_case5_runs(self):
dataset_path = os.path.join(PATH_CHRONICS, "rte_case5_example")
with make2(dataset_path) as env:
assert env.redispatching_unit_commitment_availble == True
obs = env.reset()
sim_obs, reward, done, info = obs.simulate(env.action_space())
assert sim_obs != obs
def test_case14_redisp_runs(self):
dataset_path = os.path.join(PATH_CHRONICS, "rte_case14_redisp")
with make2(dataset_path) as env:
assert env.redispatching_unit_commitment_availble == True
obs = env.reset()
sim_obs, reward, done, info = obs.simulate(env.action_space())
assert sim_obs != obs
assert np.all(env._thermal_limit_a == case14_redisp_TH_LIM)
def test_l2rpn19_override_feed_kwargs(self):
dataset_path = os.path.join(PATH_CHRONICS, "rte_L2RPN_2019")
chronics_path = os.path.join(dataset_path, "chronics", "0000")
dfk = {
"chronicsClass": ChangeNothing,
"path": chronics_path,
"gridvalueClass": GridStateFromFile
}
with make2(dataset_path, data_feeding_kwargs=dfk) as env:
assert isinstance(env.chronics_handler.real_data, ChangeNothing)
def test_case14_redisp_config(self):
dataset_path = os.path.join(PATH_CHRONICS, "rte_case14_redisp")
with make2(dataset_path) as env:
# Check config is loaded from config.py
assert env.rewardClass == RedispReward
assert env.actionClass == TopoAndRedispAction
assert env.observationClass == CompleteObservation
assert isinstance(env.backend, PandaPowerBackend)
assert env.legalActClass == DefaultRules
assert isinstance(env.voltage_controler, ControlVoltageFromFile)
assert isinstance(env.chronics_handler.real_data, Multifolder)
def test_l2rpn19_test_config(self):
dataset_path = os.path.join(PATH_CHRONICS, "rte_L2RPN_2019")
with make2(dataset_path) as env:
# Check config is loaded from config.py
assert env.rewardClass == L2RPNReward
assert env.actionClass == TopologyAction
assert env.observationClass == CompleteObservation
assert isinstance(env.backend, PandaPowerBackend)
assert env.legalActClass == DefaultRules
assert isinstance(env.voltage_controler, ControlVoltageFromFile)
assert isinstance(env.chronics_handler.real_data, Multifolder)
assert env.action_space.grid_layout != None
default=1000,
type=int,
help="Maximum number of steps per scenario")
return parser.parse_args()
if __name__ == "__main__":
args = cli()
# Limit gpu usage
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Create dataset env
env = make2(args.path_data,
reward_class=RedispReward,
action_class=CustomAction)
# Create agent
agent = RDQNAgent(env, env.action_space, is_training=False)
# Load weights from file
agent.load_network(args.path_model)
# Build runner
runner_params = env.get_params_for_runner()
runner = Runner(**runner_params, agentClass=None, agentInstance=agent)
# Run
res = runner.run(path_save=args.path_logs,
nb_episode=args.nb_episode,
nb_process=args.nb_process,
impact = action.impact_on_objects()
pruned_impact = {
"injection": prune_impact_bool(impact["injection"], "changed"),
"line_reconnect": prune_impact_count(impact["force_line"]["reconnections"], "count"),
"line_disconnect": prune_impact_count(impact["force_line"]["disconnections"], "count"),
"connect_bus": prune_impact_array(impact["topology"], "assigned_bus"),
"disconnect_bus": prune_impact_array(impact["topology"], "disconnect_bus"),
"switch_line": prune_impact_array(impact["switch_line"], "powerlines"),
"switch_bus": prune_impact_array(impact["topology"], "bus_switch"),
"redispatch": prune_impact_array(impact["redispatch"], "generators")
}
compact_impact = {k: v for k, v in pruned_impact.items() if v is not None}
actions_dict[index_str] = compact_impact
actions_json = json.dumps(actions_dict,
indent=2,
cls=NpEncoder)
print (actions_json)
if __name__ == "__main__":
args = cli()
env = make2(args.path_data, action_class=CustomAction)
# Limit gpu usage
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
agent = DoubleDuelingDQNAgent(env, env.action_space,
is_training=False)
print_actions(agent)
"connect_bus":
prune_impact_array(impact["topology"], "assigned_bus"),
"disconnect_bus":
prune_impact_array(impact["topology"], "disconnect_bus"),
"switch_line":
prune_impact_array(impact["switch_line"], "powerlines"),
"switch_bus":
prune_impact_array(impact["topology"], "bus_switch"),
"redispatch":
prune_impact_array(impact["redispatch"], "generators")
}
compact_impact = {
k: v
for k, v in pruned_impact.items() if v is not None
}
actions_dict[index_str] = compact_impact
actions_json = json.dumps(actions_dict, indent=2, cls=NpEncoder)
print(actions_json)
if __name__ == "__main__":
args = cli()
env = make2(args.path_data, action_class=PowerlineChangeAndDispatchAction)
# Limit gpu usage
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
agent = DoubleDuelingDQN(env, env.action_space, is_training=False)
print_actions(agent)
def test_l2rpn19_override_chronics(self):
dataset_path = os.path.join(PATH_CHRONICS, "rte_L2RPN_2019")
with make2(dataset_path, chronics_class=ChangeNothing) as env:
assert isinstance(env.chronics_handler.real_data, ChangeNothing)
def test_l2rpn19_override_action(self):
dataset_path = os.path.join(PATH_CHRONICS, "rte_L2RPN_2019")
with make2(dataset_path, action_class=VoltageOnlyAction) as env:
assert env.actionClass == VoltageOnlyAction
def test_case14_test_override_action(self):
dataset_path = os.path.join(PATH_CHRONICS, "rte_case14_test")
with make2(dataset_path, action_class=VoltageOnlyAction) as env:
assert env.actionClass == VoltageOnlyAction
def test_l2rpn19_override_reward(self):
dataset_path = os.path.join(PATH_CHRONICS, "rte_L2RPN_2019")
with make2(dataset_path, reward_class=FlatReward) as env:
assert env.rewardClass == FlatReward
def test_case14_test_override_reward(self):
dataset_path = os.path.join(PATH_CHRONICS, "rte_case14_test")
with make2(dataset_path, reward_class=FlatReward) as env:
assert env.rewardClass == FlatReward
required=False,
default=1e-5,
type=float,
help="Learning rate for the Adam optimizer")
parser.add_argument("--resume",
required=False,
help="Path to model.h5 to resume training with")
return parser.parse_args()
if __name__ == "__main__":
args = cli()
env = make2(args.path_data,
action_class=CustomAction,
reward_class=RedispReward,
other_rewards={
"bridge": BridgeReward,
"close_to_of": CloseToOverflowReward,
"distance": DistanceReward
})
# Limit gpu usage
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
agent = DDDQNAgent(env,
env.action_space,
name=args.name,
is_training=True,
batch_size=args.batch_size,
num_frames=args.num_frames,
lr=args.learning_rate)
