def test_dqn():
args = parse_test_dqn_args()
settings_file = os.path.join(args.model, SETTINGS_SAVEFILE)
modelfile = os.path.join(args.model, MODEL_FILE)
settings = load_settings(DEFAULT_DQN_SETTINGS_FILE, [settings_file])
_test_common(args, settings)
from _dqn_algo import DQN
dqn = DQN(**settings)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.InteractiveSession(config=config)
session.run(tf.global_variables_initializer())
dqn.load_model(session, modelfile)
log("\nScores: ")
scores = []
for _ in range(args.episodes_num):
reward = dqn.run_test_episode(session)
scores.append(reward)
print("{0:3f}".format(reward))
print()
log("\nMean score: {:0.3f}".format(np.mean(scores)))
def train_adqn():
args = parse_train_adqn_args()
settings = load_settings(DEFAULT_ADQN_SETTINGS_FILE, args.settings_yml)
_train_common(settings)
from _async_algo import train_async
train_async(q_learning=True, settings=settings)
def test_adqn():
args = parse_test_adqn_args()
settings = load_settings(DEFAULT_ADQN_SETTINGS_FILE, args.settings_yml)
_test_common(args, settings)
from _async_algo import test_async
test_async(q_learning=True,
settings=settings,
modelfile=args.model,
eps=args.episodes_num)
def train_adqn():
args = parse_train_adqn_args()
settings = load_settings(DEFAULT_ADQN_SETTINGS_FILE, args.settings_yml)
if args.run_tag is not None:
settings["run_tag"] = args.run_tag
if args.frameskip is not None:
settings["frameskip"] = args.frameskip
model_savefile = _train_common(settings)
from _async_algo import train_async
train_async(model_savefile=model_savefile, q_learning=True, settings=settings)
def test_adqn():
args = parse_test_adqn_args()
settings_file = os.path.join(args.model, SETTINGS_SAVEFILE)
modelfile = os.path.join(args.model, MODEL_FILE)
settings = load_settings(DEFAULT_ADQN_SETTINGS_FILE, [settings_file])
_test_common(args, settings)
from _async_algo import test_async
test_async(q_learning=True,
settings=settings,
modelfile=modelfile,
eps=args.episodes_num,
deterministic=args.deterministic,
output=args.output)
def train_dqn():
args = parse_train_dqn_args()
settings = load_settings(DEFAULT_DQN_SETTINGS_FILE, args.settings_yml)
_train_common(settings)
from _dqn_algo import DQN
dqn = DQN(**settings)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.InteractiveSession(config=config)
session.run(tf.global_variables_initializer())
dqn.train(session)
def train_dqn():
args = parse_train_dqn_args()
settings = load_settings(DEFAULT_DQN_SETTINGS_FILE, args.settings_yml)
if args.run_tag is not None:
settings["run_tag"] = args.run_tag
model_savefile = _train_common(settings)
if args.frameskip is not None:
settings["frameskip"] = args.frameskip
from _dqn_algo import DQN
dqn = DQN(model_savefile=model_savefile, **settings)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.InteractiveSession(config=config)
session.run(tf.global_variables_initializer())
dqn.train(session)
