import tensorflow as tf
import tensorflow.contrib.layers as layers
def layer_norm_fn(x, relu=True):
x = layers.layer_norm(x, scale=True, center=True)
if relu:
x = tf.nn.relu(x)
return x
def model(img_in, num_actions, scope, reuse=False, layer_norm=False, distributed=False, atoms=51):
"""As described in https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf"""
print("create default model: distributed? ", distributed, "atoms", atoms)
with tf.variable_scope(scope, reuse=reuse):
out = img_in
with tf.variable_scope("convnet"):
# original architecture
out = layers.convolution2d(out, num_outputs=32, kernel_size=8, stride=4, activation_fn=tf.nn.relu)
out = layers.convolution2d(out, num_outputs=64, kernel_size=4, stride=2, activation_fn=tf.nn.relu)
out = layers.convolution2d(out, num_outputs=64, kernel_size=3, stride=1, activation_fn=tf.nn.relu)
conv_out = layers.flatten(out)
with tf.variable_scope("action_value"):
value_out = layers.fully_connected(conv_out, num_outputs=512, activation_fn=None)
if layer_norm:
value_out = layer_norm_fn(value_out, relu=True)
else:
value_out = tf.nn.relu(value_out)
value_out = layers.fully_connected(value_out, num_outputs=num_actions*atoms, activation_fn=None)
if distributed:
value_out = tf.reshape(value_out, [-1, num_actions, atoms])
print("output shape:", tf.shape(value_out))
return value_out
def dueling_model(img_in, num_actions, scope, reuse=False, layer_norm=False, distributed=False, atoms=51):
"""As described in https://arxiv.org/abs/1511.06581"""
print("create dueling model: distributed? ", distributed, "atoms", atoms)
with tf.variable_scope(scope, reuse=reuse):
out = img_in
with tf.variable_scope("convnet"):
# original architecture
out = layers.convolution2d(out, num_outputs=32, kernel_size=8, stride=4, activation_fn=tf.nn.relu)
out = layers.convolution2d(out, num_outputs=64, kernel_size=4, stride=2, activation_fn=tf.nn.relu)
out = layers.convolution2d(out, num_outputs=64, kernel_size=3, stride=1, activation_fn=tf.nn.relu)
conv_out = layers.flatten(out)
with tf.variable_scope("state_value"):
state_hidden = layers.fully_connected(conv_out, num_outputs=512, activation_fn=None)
if layer_norm:
state_hidden = layer_norm_fn(state_hidden, relu=True)
else:
state_hidden = tf.nn.relu(state_hidden)
state_score = layers.fully_connected(state_hidden, num_outputs=atoms, activation_fn=None)
with tf.variable_scope("action_value"):
actions_hidden = layers.fully_connected(conv_out, num_outputs=512, activation_fn=None)
if layer_norm:
actions_hidden = layer_norm_fn(actions_hidden, relu=True)
else:
actions_hidden = tf.nn.relu(actions_hidden)
action_scores = layers.fully_connected(actions_hidden, num_outputs=num_actions*atoms, activation_fn=None)
if distributed:
atoms_action_adv = tf.reshape(action_scores, [-1, num_actions, atoms])
atoms_action_adv_mean = tf.reduce_mean(atoms_action_adv, 1)
atoms_action_adv = atoms_action_adv - tf.expand_dims(atoms_action_adv_mean, 1)
atoms_scores = tf.expand_dims(state_score, 1)
print("output shape:", tf.shape(atoms_scores))
return tf.add(atoms_scores, atoms_action_adv)
else:
action_scores_mean = tf.reduce_mean(action_scores, 1)
action_scores = action_scores - tf.expand_dims(action_scores_mean, 1)
print("output shape:", tf.shape(action_scores))
return state_score + action_scores
