Just thought I'd point out that we also did this in the 2009 Reinforcement Learning Competition (I was the general chair):

http://2009.rl-competition.org

We also used Infinite Mario Bros, but combined it with the RL-glue coding framework to make the interface easier. That way, a well-coded agent is automatically compatible with any other domain that is RL-glue compatible.

The prizes were also comparable: ~$450 for the first place team, ~$250 for the second place team.

The results were interesting: far from developing interesting and novel RL algorithms, most competitors used clever feature engineering combined with dimensionality reduction to reduce the full Mario problem to a simpler one that could be solved efficiently using existing RL algorithms that are robust and well understood.

One of the big lessons that we took away from this was that we haven't solved the mechanism design problem of competitions in AI. While Mario sounds like a good "grand challenge" problem for RL / AI, it turns out that simple heuristics work pretty well. I think this is a common problem for most of these competitions -- there's the Trading Agent Competition, there's Netflix, there's the General Game Playing Competition, etc. They all have the same goals, and they all have the same problem: competitors engineer algorithms to solve the competition, not to spur progress in general AI. These games are all a proxy for what we really care about (like the Turing test), and the proxy isn't perfect (like the Turing test).

I think the only way to get around this is to craft a domain that mimics the real world, because then if anyone "solves the competition," you've made progress on what you really care about.

It would be interesting to design a competition with these goals in mind. Maybe an extraordinary complex simulator based on a physics engine (Bullet or Havok) would be a step in the right direction -- different objects with continuous, high-dimensional state spaces and complex material properties (some are soft, some are rigid, some break, etc); interesting physical interactions between objects (collisions, joints, hinges, stacking, breaking, etc.); multiple levels of spatio-temporal abstraction (from low-level motor control to abstract tasks) and a strong vision component. Now that would be a cool competition!

David Wingate
wingated@mit.edu