There was a specific set of algorithms that got me thinking about this topic, but now that I'm thinking about the topic I'd like to look at more stuff. I would proceed by identifying spaces of policies within a domain, and then looking for learning algorithms that deal with those sorts of spaces. For sequential decision-making problems in simple settings, dynamic bayesian networks can be used both as models of an agent's environment and as action policies.

I'd be interested in talking. You can e-mail me at peter@spaceandgames.com.