本文整理匯總了Python中baselines.common.tf_util.Uint8Input方法的典型用法代碼示例。如果您正苦於以下問題：Python tf_util.Uint8Input方法的具體用法？Python tf_util.Uint8Input怎麽用？Python tf_util.Uint8Input使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類baselines.common.tf_util的用法示例。
在下文中一共展示了tf_util.Uint8Input方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: main
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import Uint8Input [as 別名]
def main():
set_global_seeds(1)
args = parse_args()
with U.make_session(4) as sess: # noqa
_, env = make_env(args.env)
model_parent_path = distdeepq.parent_path(args.model_dir)
old_args = json.load(open(model_parent_path + '/args.json'))
act = distdeepq.build_act(
make_obs_ph=lambda name: U.Uint8Input(env.observation_space.shape, name=name),
p_dist_func=distdeepq.models.atari_model(),
num_actions=env.action_space.n,
dist_params={'Vmin': old_args['vmin'],
'Vmax': old_args['vmax'],
'nb_atoms': old_args['nb_atoms']})
U.load_state(os.path.join(args.model_dir, "saved"))
wang2015_eval(args.env, act, stochastic=args.stochastic) 示例2: main
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import Uint8Input [as 別名]
def main():
set_global_seeds(1)
args = parse_args()
with U.make_session(4) as sess: # noqa
_, env = make_env(args.env)
act = deepq.build_act(
make_obs_ph=lambda name: U.Uint8Input(env.observation_space.shape, name=name),
q_func=dueling_model if args.dueling else model,
num_actions=env.action_space.n)
U.load_state(os.path.join(args.model_dir, "saved"))
wang2015_eval(args.env, act, stochastic=args.stochastic) 示例3: main
# 需要導入模塊: from baselines.common import tf_util [as 別名]
# 或者: from baselines.common.tf_util import Uint8Input [as 別名]
def main():
set_global_seeds(1)
args = parse_args()
with U.make_session(4): # noqa
_, env = make_env(args.env)
act = deepq.build_act(
make_obs_ph=lambda name: U.Uint8Input(env.observation_space.shape, name=name),
q_func=dueling_model if args.dueling else model,
num_actions=env.action_space.n)
U.load_state(os.path.join(args.model_dir, "saved"))
wang2015_eval(args.env, act, stochastic=args.stochastic)