Was DeepMind's DQN Atari game learning simultaneous? DeepMind state that their deep Q-network (DQN) was able to continually adapt its behavior while learning to play 49 Atari games.  

After learning all games with the same neural net, was the agent able to play them all at 'superhuman' levels simultaneously (whenever it was randomly presented with one of the games) or could it only be good at one game at a time because switching required a re-learn?
Switching required a re-learn.

Also, note that:


  We use the same network architecture, learning
  algorithm and hyperparameters settings across all seven games, showing that our approach is robust
  enough to work on a variety of games without incorporating game-specific information. While we
  evaluated our agents on the real and unmodified games, we made one change to the reward structure
  of the games during training only.


and 


  the
  network has outperformed all previous RL algorithms on six of the seven games we have attempted
  and surpassed an expert human player on three of them.