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)