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Learning Against Multiple Opponents by Thuc Vu; Rob Powers; Yoav Shoham is a scholarly article available to read on EtoBox.

What is Learning Against Multiple Opponents about?

We address the problem of learning in repeated n-player (as opposed to 2-player) general-sum games, paying particular attention to the rarely addressed situation in which there are a mixture of agents of different types. We propose new criteria requiring that the agents employing a particular learning algorithm work together to achieve a joint best-response against a target class of opponents, while guaranteeing they each achieve at least their individual security-level payoff against any possible set of opponents. We then provide algorithms that provably meet these criteria for two target classes: stationary strategies and adaptive strategies with a bounded memory. We also demonstrate that the algorithm for stationary strategies outperforms existing algorithms in tests spanning a wide variety of repeated games with more than two players.

Author

Thuc Vu; Rob Powers; Yoav Shoham

Publisher

ACM

Published

2006

Language

EN