'-e', '--env_name', type=str, help= 'Name of the environment. Can be either a any gym environment or a custom one defined in rl.environments' ) parser.add_argument( '-s', '--subdir', type=str, help= 'Subdirectory where the model is stored: e.g. -> ../trained_models/env_type/env/[SUBDIR]/model_num/*' ) parser.add_argument( '-n', '--num', type=int, help= 'Unique identifier of the model, e.g. -> ../trained_models/env_type/env/subdir/[NUM]_/*' ) parser.add_argument('-t', '--tensorboard', action='store_true', help='Launch tensorboard in the current subdirectory.') args = parser.parse_args() model = Trainer(args.env_name, args.subdir).load_model(args.num) if args.tensorboard: model._tensorboard() model.train()
parser.add_argument('-c', '--config', type=str, default=None, help='Adusted configuration file located in config/custom folder') parser.print_help() args = parser.parse_args() path = pathlib.Path().absolute() trainer = Trainer(args.environment, args.subdir) if args.config is not None: try: config_path = join(path, 'rl', 'config', 'custom', '{}.yml'.format(args.config)) with open(config_path) as f: config = yaml.safe_load(f) print('\nLoaded config file from: {}\n'.format(config_path)) except: print('specified config is not in path, getting original config: {}.yml...'.format(args.environment)) # load config and variables needed config = get_parameters(args.environment) else: config = get_parameters(args.environment) if args.model is not None: config['main']['model'] = args.model trainer.create_model(name=args.name, config_file=config) trainer._tensorboard() t0 = time.time() trainer.train() ts = time.time() print('Running time for training: {} minutes.'.format((ts-t0)/60)) #trainer.run(1000) trainer._save()