import tensorflow as tf

                    tf.summary.scalar('policy_loss', policy_loss)
                    tf.summary.scalar('qf1_loss', qf1_loss)
                    tf.summary.scalar('qf2_loss', qf2_loss)
                    tf.summary.scalar('value_loss', value_loss)
                    tf.summary.scalar("Imitation_loss",self.actor_loss_di)
                    tf.summary.scalar('entropy', self.entropy)
                    tf.summary.scalar('importance weight',tf.reduce_mean(self.weight_ph))
                    if ent_coef_loss is not None:
                        tf.summary.scalar('ent_coef_loss', ent_coef_loss)
                        tf.summary.scalar('ent_coef', self.ent_coef)

                    tf.summary.scalar('learning_rate', tf.reduce_mean(self.learning_rate_ph))

                # Retrieve parameters that must be saved
                self.params = tf_util.get_trainable_vars("model")
                self.target_params = tf_util.get_trainable_vars("target/values_fn/vf")

                # Initialize Variables and target network
