random_episodes_to_run = 0
action_length_reward_bonus = 0.0
only_train_new_actions = True
only_train_final_layer = True
reduce_macro_action_appearance_cutoff_throughout_training = False
add_1_macro_action_at_a_time = True
calculate_q_values_as_increments = True
abandon_ship = True
clip_rewards = True
use_relative_counts = True
config.debug_mode = False
config.hyperparameters = {
"HRL": {
"linear_hidden_units": linear_hidden_units,
"learning_rate": learning_rate,
"buffer_size": buffer_size,
"batch_size": batch_size,
"final_layer_activation": "None",
# "columns_of_data_to_be_embedded": [0],
# "embedding_dimensions": [[config.environment.observation_space.n, embedding_dimensionality]],
"batch_norm": batch_norm,
"gradient_clipping_norm": gradient_clipping_norm,
"update_every_n_steps": update_every_n_steps,
"epsilon_decay_rate_denominator": epsilon_decay_rate_denominator,
"discount_rate": discount_rate,
config.environment = Wrapper(SimpleISC(mode="DISCRETE"))
config.num_episodes_to_run = 50
config.file_to_save_data_results = "results/data_and_graphs/isc/IllinoisSolarCar_Results_Data.pkl"
config.file_to_save_results_graph = "results/data_and_graphs/isc/IllinoisSolarCar_Results_Graph.png"
config.show_solution_score = True
config.visualise_individual_results = True
config.visualise_overall_agent_results = True
config.standard_deviation_results = 1.0
config.runs_per_agent = 1
config.use_GPU = False
config.overwrite_existing_results_file = True
config.randomise_random_seed = False
config.save_model = False
config.seed = 0
config.debug_mode = True
config.hyperparameters = {
"DQN_Agents": {
"learning_rate": 0.005,
"batch_size": 128,
"buffer_size": 100000,
"epsilon": 1.0,
"epsilon_decay_rate_denominator": 150,
"discount_rate": 0.999,
"alpha_prioritised_replay": 0.6,
"beta_prioritised_replay": 0.1,
"incremental_td_error": 1e-8,
"update_every_n_steps": 15,
"tau": 1e-2,
