A "Double DQN" system is an implementation of both the Double Q Learning Algorithm and a DQN.

In order to fully implement a Double DQN, two DQN networks would be trained in parallel, similarly to how two tabular datasets are trained in traditional Double Q Learning.

The paper proposing the idea of a Double DQN used stored experience replay values instead to simulate the existence of a secondary network.

As these values were already being used to train the traditional DQN network, the performance benefits are of zero cost in comparison.