A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Multiagent learning using a variable learning rate
Artificial Intelligence
RVσ(t): a unifying approach to performance and convergence in online multiagent learning
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
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We present a regret-based multiagent learning algorithm which is provably guaranteed to converge (during self-play) to the set of Nash equilibrium in a wide class of games. Our algorithm, FRAME, consults experts in order to obtain strategy suggestions for agents. If the experts provide effective advice for the agent, then the learning process will quickly reach a desired outcome. If, however, the experts do not provide good advice, then the agents using our algorithm are still protected. We further expand our algorithm so that agents learn, not only how to play against the other agents in the environment, but also which experts are providing the most effective advice for the situation at hand.