The problem with real world tasks is you can’t really train on them.. you train on a simulation (because its much, much faster/cheaper to run [and debug], thus

The problem with real world tasks is you can’t really train on them.. you train on a simulation (because its much, much faster/cheaper to run [and debug], thus faster to train) and deploy/test in the real world. 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.