import tensorflow as tf

                    target_init_op = [
                        tf.assign(target, source)
                        for target, source in zip(target_params, source_params)
                    ]

                    # Control flow is used because sess.run otherwise evaluates in nondeterministic order
                    # and we first need to compute the policy action before computing q values losses
                    with tf.control_dependencies([policy_train_op]):
                        train_values_op = value_optimizer.minimize(values_losses, var_list=values_params)

                        self.infos_names = ['policy_loss', 'qf1_loss', 'qf2_loss', 'value_loss', 'entropy']
                        # All ops to call during one training step
                        self.step_ops = [policy_loss, qf1_loss, qf2_loss,
                                         value_loss, qf1, qf2, value_fn, logp_pi,
