import os import sys from os.path import dirname, abspath sys.path.append(dirname(dirname(abspath(__file__)))) import gym 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)
from agents.policy_gradient_agents.REINFORCE import REINFORCE from environments.FaceDiscreete import FaceEnvironementDiscreete from agents.Trainer import Trainer from utilities.data_structures.Config import Config config = Config() config.seed = 1 config.environment = FaceEnvironementDiscreete( "../weights/blg_small_12_5e-06_5e-05_2_8_small_big_noisy_first_True_512") config.num_episodes_to_run = 500 config.file_to_save_data_results = "Data_and_Graphs/FaceDiscreete.pkl" config.file_to_save_results_graph = "Data_and_Graphs/FaceDiscreete.png" config.show_solution_score = True 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 = True config.randomise_random_seed = True config.save_model = True actor_critic_agent_hyperparameters = { "Actor": { "learning_rate": 0.0003, "linear_hidden_units": [64, 64], "final_layer_activation": None, "batch_norm": False,