from ExperienceReplay import ExperienceReplay from Utility import Utility from Utility import Config env = gym.make('GazeboTurtlebotMazeColor-v0') observation = env.reset #set parameter config = Config() config.path = "./DQN_maze_target_v9" if not os.path.exists(config.path): os.makedirs(config.path) config.loadOldFile() config.saveOldFile() config.load_model = False config.pre_train_step = 1000 config.epsilon_decay = 1.0 / 1000 config.gamma = 0.99 network = Qnetwork(env.num_state, env.num_action) replay = ExperienceReplay(config.path) utility = Utility(config.path + config.reward_file, config.path + config.step_file) ######load data####### start_time = time.time() if config.load_model == True: print('loading model....') if (os.path.isfile(config.path + "/model.h5")):
env = gym.make('GazeboTurtlebotMazeColor-v0') observation = env.reset #set parameter config = Config() config.path = "./DQN_maze_target_v9" if not os.path.exists(config.path): os.makedirs(config.path) config.loadOldFile() config.saveOldFile() config.load_model = True config.pre_train_step = 1000 config.epsilon_decay = 1.0/1000 config.gamma = 0.99 network = Qnetwork(env.num_state, env.num_action) replay = ExperienceReplay(config.path) utility = Utility(config.path + config.reward_file, config.path + config.step_file) ######load data####### start_time = time.time() if config.load_model == True: print('loading model....')