It is also time to move on to real world environments. Games are cool, but the network is essentially learning some thousand lines of C++. Reinforcement learning needs to prove itself in real-world tasks.
Watch this and learn a bit more about why they're doing Starcraft AI. The Deepmind guys first appear around 32:00.
So simulation is always a part of it, regardless of target execution environment. In which case, games make a lot of sense, since they’re already complex simulations (thus useful for learning how to train AI’s, and what parts of the simulation are worth having), make good PR, can be tested against quickly, and in the case of competitive games, have a set of “experts” that already study what the human-powered collective AI learned over time, and thus it comes with a set of known expectations of how the AI might fail, and what we expect it to learn.
So tldr; simulation vs real world is a red herring. It’s the same learning process, just different execution environments.