Zinkevich's online convex optimization ( http://www.cs.cmu.edu/~maz/publications/ICML03.pdf ) generalizes regret minimization learning algorithms from a linear settings to a convex setting and gives good external regret.  Is there a similar generalization for internal regret?  (I'm not totally sure even what exactly that would mean.)
Internal Regret in Online Convex Optimization
lg.learning;online learning
Try No-regret learning in convex games by Gordon, Greenwald and Marks http://portal.acm.org/citation.cfm?id=1390202 . Its abstract sounds like it probably answers your question, or at least anyone answering that question would cite or be cited by that paper.