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[rllib] format with yapf (#2427)

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* initial yapf

* manual fix yapf bugs
This commit is contained in:
Eric Liang
2018-07-19 15:30:36 -07:00
committed by GitHub
parent 24eb140e07
commit d01dc9e22d
86 changed files with 1276 additions and 978 deletions
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python/ray/rllib/models/action_dist.py
+30 -26
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@@ -42,25 +42,25 @@ class Categorical(ActionDistribution):
logits=self.inputs, labels=x)
def entropy(self):
a0 = self.inputs - tf.reduce_max(self.inputs, reduction_indices=[1],
keepdims=True)
a0 = self.inputs - tf.reduce_max(
self.inputs, reduction_indices=[1], keepdims=True)
ea0 = tf.exp(a0)
z0 = tf.reduce_sum(ea0, reduction_indices=[1], keepdims=True)
p0 = ea0 / z0
return tf.reduce_sum(p0 * (tf.log(z0) - a0), reduction_indices=[1])
def kl(self, other):
a0 = self.inputs - tf.reduce_max(self.inputs, reduction_indices=[1],
keepdims=True)
a1 = other.inputs - tf.reduce_max(other.inputs, reduction_indices=[1],
keepdims=True)
a0 = self.inputs - tf.reduce_max(
self.inputs, reduction_indices=[1], keepdims=True)
a1 = other.inputs - tf.reduce_max(
other.inputs, reduction_indices=[1], keepdims=True)
ea0 = tf.exp(a0)
ea1 = tf.exp(a1)
z0 = tf.reduce_sum(ea0, reduction_indices=[1], keepdims=True)
z1 = tf.reduce_sum(ea1, reduction_indices=[1], keepdims=True)
p0 = ea0 / z0
return tf.reduce_sum(p0 * (a0 - tf.log(z0) - a1 + tf.log(z1)),
reduction_indices=[1])
return tf.reduce_sum(
p0 * (a0 - tf.log(z0) - a1 + tf.log(z1)), reduction_indices=[1])
def sample(self):
return tf.squeeze(tf.multinomial(self.inputs, 1), axis=1)
@@ -90,22 +90,23 @@ class DiagGaussian(ActionDistribution):
self.std = tf.exp(log_std)
def logp(self, x):
return (-0.5 * tf.reduce_sum(tf.square((x - self.mean) / self.std),
reduction_indices=[1]) -
return (-0.5 * tf.reduce_sum(
tf.square((x - self.mean) / self.std), reduction_indices=[1]) -
0.5 * np.log(2.0 * np.pi) * tf.to_float(tf.shape(x)[1]) -
tf.reduce_sum(self.log_std, reduction_indices=[1]))
def kl(self, other):
assert isinstance(other, DiagGaussian)
return tf.reduce_sum(other.log_std - self.log_std +
(tf.square(self.std) +
tf.square(self.mean - other.mean)) /
(2.0 * tf.square(other.std)) - 0.5,
reduction_indices=[1])
return tf.reduce_sum(
other.log_std - self.log_std +
(tf.square(self.std) + tf.square(self.mean - other.mean)) /
(2.0 * tf.square(other.std)) - 0.5,
reduction_indices=[1])
def entropy(self):
return tf.reduce_sum(self.log_std + .5 * np.log(2.0 * np.pi * np.e),
reduction_indices=[1])
return tf.reduce_sum(
self.log_std + .5 * np.log(2.0 * np.pi * np.e),
reduction_indices=[1])
def sample(self):
out = self.mean + self.std * tf.random_normal(tf.shape(self.mean))
@@ -158,6 +159,7 @@ class MultiActionDistribution(ActionDistribution):
Args:
inputs (Tensor list): A list of tensors from which to compute samples.
"""
def __init__(self, inputs, action_space, child_distributions):
# you actually have to instantiate the child distributions
self.reshaper = Reshaper(action_space.spaces)
@@ -174,23 +176,25 @@ class MultiActionDistribution(ActionDistribution):
# Remove extra categorical dimension
if isinstance(distribution, Categorical):
split_list[i] = tf.squeeze(split_list[i], axis=-1)
log_list = np.asarray([distribution.logp(split_x) for
distribution, split_x in
zip(self.child_distributions, split_list)])
log_list = np.asarray([
distribution.logp(split_x) for distribution, split_x in zip(
self.child_distributions, split_list)
])
return np.sum(log_list)
def kl(self, other):
"""The KL-divergence between two action distributions."""
kl_list = np.asarray([distribution.kl(other_distribution) for
distribution, other_distribution in
zip(self.child_distributions,
other.child_distributions)])
kl_list = np.asarray([
distribution.kl(other_distribution)
for distribution, other_distribution in zip(
self.child_distributions, other.child_distributions)
])
return np.sum(kl_list)
def entropy(self):
"""The entropy of the action distribution."""
entropy_list = np.array([s.entropy() for s in
self.child_distributions])
entropy_list = np.array(
[s.entropy() for s in self.child_distributions])
return np.sum(entropy_list)
def sample(self):