from environments.DMP_Env_1D_dynamic import deep_mobile_printing_1d1r config = Config() config.seed = 1 config.environment = deep_mobile_printing_1d1r() config.num_episodes_to_run = 10000 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 = True config.GPU = "cuda:0" config.overwrite_existing_results_file = True config.randomise_random_seed = False config.save_model = False OUT_FILE_NAME = "SAC_1d" + "sin" + "_seed_" + str(config.seed) config.save_model_path = "/mnt/NAS/home/WenyuHan/SNAC/SAC/1D/dynamic/" + OUT_FILE_NAME + "/" config.file_to_save_data_results = "/mnt/NAS/home/WenyuHan/SNAC/SAC/1D/dynamic/" + OUT_FILE_NAME + "/" + "Results_Data.pkl" config.file_to_save_results_graph = "/mnt/NAS/home/WenyuHan/SNAC/SAC/1D/dynamic/" + OUT_FILE_NAME + "/" + "Results_Graph.png" if os.path.exists(config.save_model_path) == False: os.makedirs(config.save_model_path) config.hyperparameters = { "Actor_Critic_Agents": { "learning_rate": 0.005, "linear_hidden_units": [20, 10], "final_layer_activation": ["SOFTMAX", None], "gradient_clipping_norm": 5.0, "discount_rate": 0.99, "epsilon_decay_rate_denominator": 1.0,
config = Config() config.seed = 1 config.environment = gym.make("ObstacleAvoidance-v0") 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,