import tensorflow as tf

                    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
                with self.sess.as_default():
                    self.sess.run(tf.global_variables_initializer())
                    self.sess.run(target_init_op)

                self.summary = tf.summary.merge_all()

    def pretrain_sac(self,pretrain_steps):
        print("=====SAC Pretraining=====")
        for step in range(pretrain_steps):
            # Compute current learning_rate
            frac = 1.0 - step / pretrain_steps
            current_lr = self.learning_rate(frac)
            # Update policy and critics (q functions)
