'-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()