import gym from agents.hierarchical_agents.HRL.HRL import HRL from agents.Trainer import Trainer from utilities.data_structures.Config import Config config = Config() config.environment = gym.make("Taxi-v2") config.seed = 1 config.env_parameters = {} config.num_episodes_to_run = 2000 config.file_to_save_data_results = None config.file_to_save_results_graph = None config.show_solution_score = False config.visualise_individual_results = False config.visualise_overall_agent_results = True config.standard_deviation_results = 1.0 config.runs_per_agent = 3 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False linear_hidden_units = [32, 32] learning_rate = 0.01 buffer_size = 100000 batch_size = 256 batch_norm = False embedding_dimensionality = 10 gradient_clipping_norm = 5 update_every_n_steps = 1
from agents.hierarchical_agents.SNN_HRL import SNN_HRL from agents.Trainer import Trainer from utilities.data_structures.Config import Config from agents.DQN_agents.DQN import DQN from agents.hierarchical_agents.h_DQN import h_DQN from environments.Long_Corridor_Environment import Long_Corridor_Environment config = Config() config.seed = 1 config.env_parameters = {"stochasticity_of_action_right": 0.5} config.environment = Long_Corridor_Environment( stochasticity_of_action_right=config. env_parameters["stochasticity_of_action_right"]) config.num_episodes_to_run = 10000 config.file_to_save_data_results = "Data_and_Graphs/Long_Corridor_Results_Data.pkl" config.file_to_save_results_graph = "Data_and_Graphs/Long_Corridor_Results_Graph.png" config.show_solution_score = False config.visualise_individual_results = False config.visualise_overall_agent_results = True config.standard_deviation_results = 1.0 config.runs_per_agent = 3 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = False config.load_model = False config.hyperparameters = { "h_DQN": { "CONTROLLER": { "batch_size":