config.seed = 1 config.environment = gym.make( "Reacher-v2") # Reacher-v2 "InvertedPendulum-v2") #Pendulum-v0 config.num_episodes_to_run = 1500 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 = 1 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 = { "HIRO": { "LOWER_LEVEL": { "max_lower_level_timesteps": 5, "Actor": { "learning_rate": 0.001, "linear_hidden_units": [20, 20], "final_layer_activation": "TANH", "batch_norm": False, "tau": 0.005, "gradient_clipping_norm": 5 }, "Critic": { "learning_rate": 0.01,
# config.environment = gym.make("CartPole-v0") config.num_episodes_to_run = 50 # config.num_episodes_to_run = 450 config.file_to_save_data_results = "results/data_and_graphs/stocks_Results_Data.pkl" config.file_to_save_results_graph = "results/data_and_graphs/stocks_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 = 1 config.use_GPU = False config.overwrite_existing_results_file = False config.randomise_random_seed = True config.model_path = r'drive/My Drive/l_gym/Models/%s' % column_list_str config.save_model = False config.load_model = True config.run_test = True config.run_test_path = r"drive/My Drive/l_gym/data_and_graphs/%s/%s/{}_run_test.png" % ( symbol, column_list_str) # config.run_test_path = r"drive/My Drive/l_gym/data_and_graphs/%s/{}_run_test.png" % column_list_str try: os.makedirs(config.model_path) except: pass try: os.makedirs(r"drive/My Drive/l_gym/data_and_graphs/%s/%s" % (symbol, column_list_str)) except: pass