import tensorflow as tf

                        grads_q = tf.gradients(loss_q * self.q_coef, self.params)
                        grads = [gradient_add(g1, g2, param, verbose=self.verbose)
                                 for (g1, g2, param) in zip(grads_policy, grads_q, self.params)]

                        avg_norm_grads_f = avg_norm(grads_f) * (self.n_steps * self.n_envs)
                        norm_grads_q = tf.global_norm(grads_q)
                        norm_grads_policy = tf.global_norm(grads_policy)
                    else:
                        grads = tf.gradients(loss, self.params)

