本文整理汇总了Python中baselines.logger.warn方法的典型用法代码示例。如果您正苦于以下问题：Python logger.warn方法的具体用法？Python logger.warn怎么用？Python logger.warn使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类baselines.logger的用法示例。
在下文中一共展示了logger.warn方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: render
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import warn [as 别名]
def render(self):
logger.warn('Render not defined for %s'%self) 示例2: render
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import warn [as 别名]
def render(self, mode='human'):
logger.warn('Render not defined for %s' % self) 示例3: reset
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import warn [as 别名]
def reset(self):
if self.waiting_step:
logger.warn('Called reset() while waiting for the step to complete')
self.step_wait()
for pipe in self.parent_pipes:
pipe.send(('reset', None))
return self._decode_obses([pipe.recv() for pipe in self.parent_pipes]) 示例4: load_state
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import warn [as 别名]
def load_state(fname, sess=None):
from baselines import logger
logger.warn('load_state method is deprecated, please use load_variables instead')
sess = sess or get_session()
saver = tf.train.Saver()
saver.restore(tf.get_default_session(), fname) 示例5: save_state
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import warn [as 别名]
def save_state(fname, sess=None):
from baselines import logger
logger.warn('save_state method is deprecated, please use save_variables instead')
sess = sess or get_session()
dirname = os.path.dirname(fname)
if any(dirname):
os.makedirs(dirname, exist_ok=True)
saver = tf.train.Saver()
saver.save(tf.get_default_session(), fname)
# The methods above and below are clearly doing the same thing, and in a rather similar way
# TODO: ensure there is no subtle differences and remove one 示例6: prepare_params
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import warn [as 别名]
def prepare_params(kwargs):
# DDPG params
ddpg_params = dict()
env_name = kwargs['env_name']
def make_env(subrank=None):
env = gym.make(env_name)
if subrank is not None and logger.get_dir() is not None:
try:
from mpi4py import MPI
mpi_rank = MPI.COMM_WORLD.Get_rank()
except ImportError:
MPI = None
mpi_rank = 0
logger.warn('Running with a single MPI process. This should work, but the results may differ from the ones publshed in Plappert et al.')
max_episode_steps = env._max_episode_steps
env = Monitor(env,
os.path.join(logger.get_dir(), str(mpi_rank) + '.' + str(subrank)),
allow_early_resets=True)
# hack to re-expose _max_episode_steps (ideally should replace reliance on it downstream)
env = gym.wrappers.TimeLimit(env, max_episode_steps=max_episode_steps)
return env
kwargs['make_env'] = make_env
tmp_env = cached_make_env(kwargs['make_env'])
assert hasattr(tmp_env, '_max_episode_steps')
kwargs['T'] = tmp_env._max_episode_steps
kwargs['max_u'] = np.array(kwargs['max_u']) if isinstance(kwargs['max_u'], list) else kwargs['max_u']
kwargs['gamma'] = 1. - 1. / kwargs['T']
if 'lr' in kwargs:
kwargs['pi_lr'] = kwargs['lr']
kwargs['Q_lr'] = kwargs['lr']
del kwargs['lr']
for name in ['buffer_size', 'hidden', 'layers',
'network_class',
'polyak',
'batch_size', 'Q_lr', 'pi_lr',
'norm_eps', 'norm_clip', 'max_u',
'action_l2', 'clip_obs', 'scope', 'relative_goals']:
ddpg_params[name] = kwargs[name]
kwargs['_' + name] = kwargs[name]
del kwargs[name]
kwargs['ddpg_params'] = ddpg_params
return kwargs