The canonical DoubleDQN uses the target network. I've not seen the first version used anywhere in the deep RL literature, but it looks like what one would do if they were to take the original Double Q-Learning algorithm and place it exactly into the Deep RL format. The reason they don't do this in the Double DQN paper is because it would be very expensive to train and maintain two networks, when you are using a target network anyway that trades off performance and computational efficiency.

The canonical DoubleDQN uses the target network. I've not seen the first version used anywhere in the deep RL literature, but it looks like what one would do if they were to take the original Double Q-Learning algorithm and place it exactly into the Deep RL format. The reason they don't do this in the Double DQN paper is because it would be very expensive to train and maintain two networks, when you are using a target network anyway. I believe they found that the target network worked well enough at reducing the maximisation bias without much more computational cost to the original DQN.