import tensorflow as tf



                    # Target for value fn regression
                    # We update the vf towards the min of two Q-functions in order to
                    # reduce overestimation bias from function approximation error.
                    v_backup = tf.stop_gradient(min_qf_pi - self.ent_coef * logp_pi)
                    value_loss = 0.5 * tf.reduce_mean(((value_fn - v_backup) ** 2)*self.weight_ph)
                    
                    #value_for_priority = tf.reduce_mean((value_fn - v_backup) ** 2,1)
                    regularizervf = tf.contrib.layers.l1_l2_regularizer(scale_l1=0.0, scale_l2=1e-5, scope='model/values_fn')
