本文整理汇总了Python中baselines.common.mpi_running_mean_std.RunningMeanStd方法的典型用法代码示例。如果您正苦于以下问题：Python mpi_running_mean_std.RunningMeanStd方法的具体用法？Python mpi_running_mean_std.RunningMeanStd怎么用？Python mpi_running_mean_std.RunningMeanStd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.common.mpi_running_mean_std的用法示例。
在下文中一共展示了mpi_running_mean_std.RunningMeanStd方法的4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: build_graph
# 需要导入模块: from baselines.common import mpi_running_mean_std [as 别名]
# 或者: from baselines.common.mpi_running_mean_std import RunningMeanStd [as 别名]
def build_graph(self, obs_ph, acs_ph, reuse=False):
with tf.variable_scope(self.scope):
if reuse:
tf.get_variable_scope().reuse_variables()
with tf.variable_scope("obfilter"):
self.obs_rms = RunningMeanStd(shape=self.observation_shape)
obs = (obs_ph - self.obs_rms.mean / self.obs_rms.std)
_input = tf.concat([obs, acs_ph], axis=1) # concatenate the two input -> form a transition
p_h1 = tf.contrib.layers.fully_connected(_input, self.hidden_size, activation_fn=tf.nn.tanh)
p_h2 = tf.contrib.layers.fully_connected(p_h1, self.hidden_size, activation_fn=tf.nn.tanh)
logits = tf.contrib.layers.fully_connected(p_h2, 1, activation_fn=tf.identity)
return logits 示例2: _init
# 需要导入模块: from baselines.common import mpi_running_mean_std [as 别名]
# 或者: from baselines.common.mpi_running_mean_std import RunningMeanStd [as 别名]
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, gaussian_fixed_var=True):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(dense(last_out, hid_size, "vffc%i" % (i+1), weight_init=U.normc_initializer(1.0)))
self.vpred = dense(last_out, 1, "vffinal", weight_init=U.normc_initializer(1.0))[:, 0]
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(dense(last_out, hid_size, "polfc%i" % (i+1), weight_init=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = dense(last_out, pdtype.param_shape()[0]//2, "polfinal", U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.zeros_initializer())
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = dense(last_out, pdtype.param_shape()[0], "polfinal", U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
# change for BC
stochastic = U.get_placeholder(name="stochastic", dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self.ac = ac
self._act = U.function([stochastic, ob], [ac, self.vpred]) 示例3: _init
# 需要导入模块: from baselines.common import mpi_running_mean_std [as 别名]
# 或者: from baselines.common.mpi_running_mean_std import RunningMeanStd [as 别名]
def _init(self, ob_space, ac_space, hid_size, num_hid_layers, gaussian_fixed_var=True):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=[sequence_length] + list(ob_space.shape))
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
with tf.variable_scope('vf'):
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0, 5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(tf.layers.dense(last_out, hid_size, name="fc%i"%(i+1), kernel_initializer=U.normc_initializer(1.0)))
self.vpred = tf.layers.dense(last_out, 1, name='final', kernel_initializer=U.normc_initializer(1.0))[:,0]
with tf.variable_scope('pol'):
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(tf.layers.dense(last_out, hid_size, name='fc%i'%(i+1), kernel_initializer=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = tf.layers.dense(last_out, pdtype.param_shape()[0]//2, name='final', kernel_initializer=U.normc_initializer(0.01))
logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0]//2], initializer=tf.zeros_initializer())
pdparam = tf.concat([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = tf.layers.dense(last_out, pdtype.param_shape()[0], name='final', kernel_initializer=U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
stochastic = tf.placeholder(dtype=tf.bool, shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self._act = U.function([stochastic, ob], [ac, self.vpred]) 示例4: _normalize_clip_observation
# 需要导入模块: from baselines.common import mpi_running_mean_std [as 别名]
# 或者: from baselines.common.mpi_running_mean_std import RunningMeanStd [as 别名]
def _normalize_clip_observation(x, clip_range=[-5.0, 5.0]):
rms = RunningMeanStd(shape=x.shape[1:])
norm_x = tf.clip_by_value((x - rms.mean) / rms.std, min(clip_range), max(clip_range))
return norm_x, rms