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Code Issues Projects Releases Wiki Activity

Batch RL Tutorial (#372)

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Gal Leibovich
2019-07-14 18:43:48 +03:00
committed by GitHub
parent b82414138d
commit 19ad2d60a7
40 changed files with 1155 additions and 182 deletions
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14
rl_coach/architectures/tensorflow_components/layers.py
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@@ -168,15 +168,18 @@ class Dense(layers.Dense):
def __init__(self, units: int):
super(Dense, self).__init__(units=units)
def __call__(self, input_layer, name: str=None, kernel_initializer=None, activation=None, is_training=None):
def __call__(self, input_layer, name: str=None, kernel_initializer=None, bias_initializer=None,
activation=None, is_training=None):
"""
returns a tensorflow dense layer
:param input_layer: previous layer
:param name: layer name
:return: dense layer
"""
if bias_initializer is None:
bias_initializer = tf.zeros_initializer()
return tf.layers.dense(input_layer, self.units, name=name, kernel_initializer=kernel_initializer,
activation=activation)
activation=activation, bias_initializer=bias_initializer)
@staticmethod
@reg_to_tf_instance(layers.Dense)
@@ -199,7 +202,8 @@ class NoisyNetDense(layers.NoisyNetDense):
def __init__(self, units: int):
super(NoisyNetDense, self).__init__(units=units)
def __call__(self, input_layer, name: str, kernel_initializer=None, activation=None, is_training=None):
def __call__(self, input_layer, name: str, kernel_initializer=None, activation=None, is_training=None,
bias_initializer=None):
"""
returns a NoisyNet dense layer
:param input_layer: previous layer
@@ -233,10 +237,12 @@ class NoisyNetDense(layers.NoisyNetDense):
kernel_stddev_initializer = tf.random_uniform_initializer(-stddev * self.sigma0, stddev * self.sigma0)
else:
kernel_mean_initializer = kernel_stddev_initializer = kernel_initializer
if bias_initializer is None:
bias_initializer = tf.zeros_initializer()
with tf.variable_scope(None, default_name=name):
weight_mean = tf.get_variable('weight_mean', shape=(num_inputs, num_outputs),
initializer=kernel_mean_initializer)
bias_mean = tf.get_variable('bias_mean', shape=(num_outputs,), initializer=tf.zeros_initializer())
bias_mean = tf.get_variable('bias_mean', shape=(num_outputs,), initializer=bias_initializer)
weight_stddev = tf.get_variable('weight_stddev', shape=(num_inputs, num_outputs),
initializer=kernel_stddev_initializer)