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