env.chronics_handler.real_data.set_filter(lambda x: re.match(".*Scenario_february_.*$", x) is not None)
env.chronics_handler.real_data.reset()
elif env.name == "l2rpn_case14_sandbox":
# all data can be loaded into memory
# env.chronics_handler.real_data.set_filter(lambda x: True)
env.chronics_handler.real_data.reset()
# env.chronics_handler.real_data.
env_init = env
if args.nb_env > 1:
from l2rpn_baselines.utils import make_multi_env
env = make_multi_env(env_init=env_init, nb_env=int(args.nb_env))
tp = TrainingParam()
# NN training
tp.lr = 1e-5
tp.lr_decay_steps = 300000
tp.minibatch_size = 32 * int(args.nb_env)
tp.update_freq = tp.minibatch_size / 2
# limit the number of time steps played per scenarios
tp.step_increase_nb_iter = None # None to deactivate it
tp.min_iter = None
tp.update_nb_iter = None # once 100 scenarios are solved, increase of "step_increase_nb_iter"
# oversampling hard scenarios
tp.oversampling_rate = None # None to deactivate it
# experience replay
tp.buffer_size = 1000000
# from grid2op.Environment import MultiEnvironment
# env = MultiEnvironment(int(args.nb_env), env)
# # TODO hack i'll fix in 1.0.0
# env.action_space = env_init.action_space
# env.observation_space = env_init.observation_space
# env.fast_forward_chronics = lambda x: None
# env.chronics_handler = env_init.chronics_handler
# env.current_obs = env_init.current_obs
# env.set_ff()
from l2rpn_baselines.utils import make_multi_env
env = make_multi_env(env_init=env_init, nb_env=int(args.nb_env))
tp = TrainingParam()
# NN training
tp.lr = 1e-4
tp.lr_decay_steps = 30000
tp.minibatch_size = 32
tp.update_freq = 16
# limit the number of time steps played per scenarios
tp.step_increase_nb_iter = 100 # None to deactivate it
tp.min_iter = 10
tp.update_nb_iter = 100 # once 100 scenarios are solved, increase of "step_increase_nb_iter"
# oversampling hard scenarios
tp.oversampling_rate = 3 # None to deactivate it
# experience replay
tp.buffer_size = 1000000
