def state_to_hidden(self, input_state, config, Collection=None):
conv_stack_shape=[(32,8,4),
(64,4,2),
(64,3,1)]
head = tf.div(input_state, 256., name="normalized_input")
cops.build_activation_summary(head, Collection)
head = cops.conv_stack(head, conv_stack_shape, config, Collection)
head = cops.flatten(head)
return head
def Q_network(self, input_state, config, Collection=None):
conv_stack_shape=[(32,8,4),
(64,4,2),
(64,3,1)]
head = tf.div(input_state,256., name='normalized_input')
head = cops.conv_stack(head, conv_stack_shape, Collection)
head = cops.flatten(head)
head = cops.add_relu_layer(head, size=512, Collection=Collection)
Q = cops.add_linear_layer(head, self.num_actions, Collection, layer_name="Q")
return Q
def Q_network(self, input_state, Collection):
conv_stack_shape=[(32,8,4),
(64,4,2),
(64,3,1)]
head = tf.div(input_state, 256., name="normalized_input")
cops.build_activation_summary(head, Collection)
head = cops.conv_stack(head, conv_stack_shape, self.config, Collection)
head = cops.flatten(head)
head = cops.add_relu_layer(head, size=512, Collection=Collection)
Q = cops.add_linear_layer(head, self.config.action_num, Collection, layer_name="Q")
# DQN summary
for i in range(self.config.action_num):
cops.build_scalar_summary(Q[0, i], Collection, "Q/Q_0_"+str(i))
return Q
def Q_network(self, input_state, Collection=None):
conv_stack_shape=[(32,8,4),
(64,4,2),
(64,3,1)]
head = tf.div(input_state, 256., name="normalized_input")
cops.build_activation_summary(head, Collection)
head = cops.conv_stack(head, conv_stack_shape, self.config, Collection)
head = cops.flatten(head)
V_head = cops.add_relu_layer(head, size=512, Collection=Collection)
V = cops.add_linear_layer(V_head, 1, Collection, layer_name="V")
A_head = cops.add_relu_layer(head, size=512, Collection=Collection)
A = cops.add_linear_layer(A_head, self.config.action_num, Collection, layer_name="A")
Q = tf.add(A, V - tf.expand_dims(tf.reduce_mean(A, axis=1)/self.config.action_num, axis=1) )
cops.build_scalar_summary(V[0], Collection, "Q/V_0")
for i in range(self.config.action_num):
cops.build_scalar_summary(Q[0, i], Collection, "Q/Q_0_"+str(i))
cops.build_scalar_summary(A[0, i], Collection, "Q/A_0_"+str(i))
return Q