Carlos Diuk, Andre Cohen, and Michael L. Littman of Littman’s RL3 Laboratory at Rutgers devised a new way of doing reinforcement learning, using Object-Oriented Markov Decision Processes, a representation that looks at a higher level than usual and considers objects and interactions. They had a paper about this at last year’s International Conference on Machine Learning (ICML). Better yet, they demonstrated their OO-MDPs representation by using it in a system that learned to play Pitfall in an emulator. I don’t believe that the system got all the treasures, but watching it play and explore the environment was certainly impressive. It seems like the technique is an interesting advance. By trying it out on a classic game, the researchers suggest that it will have plenty of “serious” uses in addition to being used in video game testing and in game AI.