A General Class of No-Regret Learning Algoritms and Game-Theoretic Equilibria
    • In this talk, we define a general class of no-regret learning algorithms, called no-Φ-regret learning algorithms, and a a class of game-theoretic equilibria, called Φ-equilibria, we show that for all φ ε Φ, the empirical distribution of play of no-φ-regret algorithms converges to the set of Φ-equilibria.
    • [ps] [pdf]
    • Presented at Caltech, October 2003. [abstract]
    • Presented at COLT, August 2003. [abstract]
    • Joint work with Amir Jafari.

    Reinforcement Learning in Stochastic Games






    The Santa Fe Bar Problem Revisited: Theoretical and Practical Implications
    • This research includes both negative and positive results on learning in the Santa Fe Bar Problem.
    • Presented at the The Stony Brook Game Theory Festival: Workshop on Interactive Dynamics and Learning, SUNY Stony Brook, July 1998.
    • Joint work with Bud Mishra and Rohit Parikh.
    • [ps] [pdf] [abstract]

    Learning in Networks Contexts: Experimental Results from Simulations

    Learning to Play Network Games

    • This presentation provides an introduction to the connection between learning, game theory, and networks.
    • DARPA Graduate Student Workshop, Washington, DC, July 1998.
    • [ps] [pdf] [abstract]