import tensorflow as tf

                        tf.summary.histogram('advantage', adv)
                        tf.summary.histogram('action_probability', self.mu_ph)
                        if tf_util.is_image(self.observation_space):
                            tf.summary.image('observation', train_model.obs_ph)
                        else:
                            tf.summary.histogram('observation', train_model.obs_ph)

                trainer = tf.train.RMSPropOptimizer(learning_rate=self.learning_rate_ph, decay=self.rprop_alpha,
                                                    epsilon=self.rprop_epsilon)
                _opt_op = trainer.apply_gradients(grads)
