import tensorflow as tf

                                                    epsilon=self.rprop_epsilon)
                _opt_op = trainer.apply_gradients(grads)

                # so when you call _train, you first do the gradient step, then you apply ema
                with tf.control_dependencies([_opt_op]):
                    _train = tf.group(ema_apply_op)

                # Ops/Summaries to run, and their names for logging
                assert norm_grads is not None
                run_ops = [_train, loss, loss_q, entropy, loss_policy, loss_f, loss_bc, explained_variance, norm_grads]
