config.num_episodes_to_run = 2000 config.file_to_save_data_results = "C:/my_project/Deep-Reinforcement-Learning-Algorithms-with-PyTorch/results/data_and_graphs/carla_obstacle_avoidance/data.pkl" config.file_to_save_results_graph = "C:/my_project/Deep-Reinforcement-Learning-Algorithms-with-PyTorch/results/data_and_graphs/carla_obstacle_avoidance/data.png" config.show_solution_score = False 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 = True config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = True config.resume = False config.resume_path = '' config.backbone_pretrain = True config.hyperparameters = { "learning_rate": 1e-2 * 10., "batch_size": 32, "buffer_size": 20000, "epsilon": 1.0, "epsilon_decay_rate_denominator": 1.0, "discount_rate": 0.99, "tau": 0.01, "alpha_prioritised_replay": 0.6, "beta_prioritised_replay": 0.1, "incremental_td_error": 1e-8, "update_every_n_steps": 1, "linear_hidden_units": [24, 48, 24], "final_layer_activation": "None",
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 = True config.overwrite_existing_results_file = False config.randomise_random_seed = True config.save_model = True config.log_loss = False config.log_base = time.strftime("%Y%m%d%H%M%S", time.localtime()) config.save_model_freq = 300 ## save model per 300 episodes config.retrain = False config.resume = False config.resume_path = 'C:\my_project\RL-based-decision-making-in-Carla\\results\Models\DDQN with Prioritised Replay\DDQN with Prioritised Replay_1500.model' config.backbone_pretrain = False config.force_explore_mode = True config.force_explore_stare_e = 0.4 ## when the std of rolling score in last 10 window is smaller than this val, start explore mode config.force_explore_rate = 0.95 ## only when the current score bigger than 0.8*max(rolling score[-10:]), forece expolre ## data and graphs save dir ## data_results_root = os.path.join(os.path.dirname(__file__)+"/data_and_graphs/carla_obstacle_avoidance", config.log_base) while os.path.exists(data_results_root): data_results_root += '_' os.makedirs(data_results_root) config.file_to_save_data_results = os.path.join(data_results_root, "data.pkl") config.file_to_save_results_graph = os.path.join(data_results_root, "data.png") config.hyperparameters = {