Example #1
0
    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

    # just observe the data for a while
    tp.min_observe = None  # int(10000)

    # e greedy
    tp.min_observation = 128
    tp.initial_epsilon = 0.2
    tp.final_epsilon = 1./(288.)
    tp.step_for_final_epsilon = int(1e5)
    # TODO add the "i dont do anything for a few time steps at the beginning of the training"

    # don't start always at the same hour (if not None) otherwise random sampling, see docs
    tp.random_sample_datetime_start = None

    # saving, logging etc.
    tp.save_model_each = 10000
    tp.update_tensorboard_freq = 256
Example #2
0
    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

    # just observe the data for a while
    tp.min_observe = int(100000)
    tp.sample_one_random_action_begin = int(tp.min_observe // 2)

    # e greedy
    tp.min_observation = 128
    tp.initial_epsilon = 0.2
    tp.final_epsilon = 1. / (288.)
    tp.step_for_final_epsilon = int(1e5)
    # TODO add the "i dont do anything for a few time steps at the beginning of the training"

    # don't start always at the same hour (if not None) otherwise random sampling, see docs
    tp.random_sample_datetime_start = None

    # saving, logging etc.
    tp.save_model_each = 10000
    tp.update_tensorboard_freq = 256