本文整理汇总了Python中baselines.logger.scoped_configure方法的典型用法代码示例。如果您正苦于以下问题：Python logger.scoped_configure方法的具体用法？Python logger.scoped_configure怎么用？Python logger.scoped_configure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.scoped_configure方法的3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: test_mpi_weighted_mean
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import scoped_configure [as 别名]
def test_mpi_weighted_mean():
comm = MPI.COMM_WORLD
with logger.scoped_configure(comm=comm):
if comm.rank == 0:
name2valcount = {'a' : (10, 2), 'b' : (20,3)}
elif comm.rank == 1:
name2valcount = {'a' : (19, 1), 'c' : (42,3)}
else:
raise NotImplementedError
d = mpi_util.mpi_weighted_mean(comm, name2valcount)
correctval = {'a' : (10 * 2 + 19) / 3.0, 'b' : 20, 'c' : 42}
if comm.rank == 0:
assert d == correctval, '{} != {}'.format(d, correctval)
for name, (val, count) in name2valcount.items():
for _ in range(count):
logger.logkv_mean(name, val)
d2 = logger.dumpkvs()
if comm.rank == 0:
assert d2 == correctval 示例2: __init__
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import scoped_configure [as 别名]
def __init__(self, env_fns, spaces=None):
"""
If you don't specify observation_space, we'll have to create a dummy
environment to get it.
"""
if spaces:
observation_space, action_space = spaces
else:
logger.log('Creating dummy env object to get spaces')
with logger.scoped_configure(format_strs=[]):
dummy = env_fns[0]()
observation_space, action_space = dummy.observation_space, dummy.action_space
dummy.close()
del dummy
VecEnv.__init__(self, len(env_fns), observation_space, action_space)
self.obs_keys, self.obs_shapes, self.obs_dtypes = obs_space_info(observation_space)
self.obs_bufs = [
{k: Array(_NP_TO_CT[self.obs_dtypes[k].type], int(np.prod(self.obs_shapes[k]))) for k in self.obs_keys}
for _ in env_fns]
self.parent_pipes = []
self.procs = []
for env_fn, obs_buf in zip(env_fns, self.obs_bufs):
wrapped_fn = CloudpickleWrapper(env_fn)
parent_pipe, child_pipe = Pipe()
proc = Process(target=_subproc_worker,
args=(child_pipe, parent_pipe, wrapped_fn, obs_buf, self.obs_shapes, self.obs_dtypes, self.obs_keys))
proc.daemon = True
self.procs.append(proc)
self.parent_pipes.append(parent_pipe)
proc.start()
child_pipe.close()
self.waiting_step = False
self.viewer = None 示例3: __init__
# 需要导入模块: from baselines import logger [as 别名]
# 或者: from baselines.logger import scoped_configure [as 别名]
def __init__(self, env_fns, spaces=None, context='spawn'):
"""
If you don't specify observation_space, we'll have to create a dummy
environment to get it.
"""
ctx = mp.get_context(context)
if spaces:
observation_space, action_space = spaces
else:
logger.log('Creating dummy env object to get spaces')
with logger.scoped_configure(format_strs=[]):
dummy = env_fns[0]()
observation_space, action_space = dummy.observation_space, dummy.action_space
dummy.close()
del dummy
VecEnv.__init__(self, len(env_fns), observation_space, action_space)
self.obs_keys, self.obs_shapes, self.obs_dtypes = obs_space_info(observation_space)
self.obs_bufs = [
{k: ctx.Array(_NP_TO_CT[self.obs_dtypes[k].type], int(np.prod(self.obs_shapes[k]))) for k in self.obs_keys}
for _ in env_fns]
self.parent_pipes = []
self.procs = []
with clear_mpi_env_vars():
for env_fn, obs_buf in zip(env_fns, self.obs_bufs):
wrapped_fn = CloudpickleWrapper(env_fn)
parent_pipe, child_pipe = ctx.Pipe()
proc = ctx.Process(target=_subproc_worker,
args=(child_pipe, parent_pipe, wrapped_fn, obs_buf, self.obs_shapes, self.obs_dtypes, self.obs_keys))
proc.daemon = True
self.procs.append(proc)
self.parent_pipes.append(parent_pipe)
proc.start()
child_pipe.close()
self.waiting_step = False
self.viewer = None