from agents.actor_critic_agents.SAC_Discrete import SAC_Discrete from agents.Trainer import Trainer from utilities.data_structures.Config import Config 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],
from utilities.data_structures.Config import Config from environments.DMP_simulator_3d_static_circle import deep_mobile_printing_3d1r PALN_CHOICE = 1 # 0 dense 1 sparse PLAN_LIST = ["dense", "sparse"] PLAN_NAME = PLAN_LIST[PALN_CHOICE] config = Config() config.seed = 5 config.environment = deep_mobile_printing_3d1r(plan_choose=PALN_CHOICE) config.num_episodes_to_run = 5000 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:1" config.overwrite_existing_results_file = True config.randomise_random_seed = False config.save_model = False OUT_FILE_NAME = "SAC_3d_" + PLAN_NAME + "_seed_" + str(config.seed) config.save_model_path = "/mnt/NAS/home/WenyuHan/SNAC/SAC/3D/static/" + OUT_FILE_NAME + "/" config.file_to_save_data_results = "/mnt/NAS/home/WenyuHan/SNAC/SAC/3D/static/" + OUT_FILE_NAME + "/" + "Results_Data.pkl" config.file_to_save_results_graph = "/mnt/NAS/home/WenyuHan/SNAC/SAC/3D/static/" + 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": [512, 512, 512], "final_layer_activation": ["SOFTMAX", None],