learning against multiple opponents
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.
learning game theory multi-agent systems