File size: 2,242 Bytes
5960497 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import tensorflow as tf
import tensorflow.contrib.layers as layers
def build_q_func(network, hiddens=[256], dueling=True, layer_norm=False, **network_kwargs):
if isinstance(network, str):
from baselines.common.models import get_network_builder
network = get_network_builder(network)(**network_kwargs)
def q_func_builder(input_placeholder, num_actions, scope, reuse=False):
with tf.compat.v1.variable_scope(scope, reuse=reuse):
latent = network(input_placeholder)
if isinstance(latent, tuple):
if latent[1] is not None:
raise NotImplementedError("DQN is not compatible with recurrent policies yet")
latent = latent[0]
latent = layers.flatten(latent)
with tf.compat.v1.variable_scope("action_value"):
action_out = latent
for hidden in hiddens:
action_out = layers.fully_connected(action_out, num_outputs=hidden, activation_fn=None)
if layer_norm:
action_out = layers.layer_norm(action_out, center=True, scale=True)
action_out = tf.nn.relu(action_out)
action_scores = layers.fully_connected(action_out, num_outputs=num_actions, activation_fn=None)
if dueling:
with tf.compat.v1.variable_scope("state_value"):
state_out = latent
for hidden in hiddens:
state_out = layers.fully_connected(state_out, num_outputs=hidden, activation_fn=None)
if layer_norm:
state_out = layers.layer_norm(state_out, center=True, scale=True)
state_out = tf.nn.relu(state_out)
state_score = layers.fully_connected(state_out, num_outputs=1, activation_fn=None)
action_scores_mean = tf.reduce_mean(input_tensor=action_scores, axis=1)
action_scores_centered = action_scores - tf.expand_dims(action_scores_mean, 1)
q_out = state_score + action_scores_centered
else:
q_out = action_scores
return q_out
return q_func_builder
|