However, there’s lots of work on using reinforcement learning algorithms of different kinds (temporal difference learning, evolution strategies etc.) to learn to play games. My previous supervisor Simon Lucas worked on learning to play Pac-Man (using the Microsoft Revenge of Arcade version), I’ve worked on various car racing games, Unreal Tournament, Super Mario Bros etc. The proceedings of the yearly Computational Intelligence and Games conferences are full of examples of this kind of research.

Apart from a good way of testing the various kinds of RL algorithms, it’s also a good way of learning about the game you’re testing, and might actually be useful in game design.

Check out these people’s work!

MDPs are a great way to formally describe video games are and (to me) RL, or value-function approximation, is a good metaphor for describing what gamers enjoy doing.