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• W82142180 abstract "Recently, a number of authors have proposed criteria for evaluating learning algorithms in multiagent systems. While well-justified, each of these has generally given little attention to one of the main challenges of a multi-agent setting: the capability of the other agents to adapt and learn as well. We propose extending existing criteria to apply to a class of adaptive opponents with bounded memory. We then show an algorithm that provably achieves an o-best response against this richer class of opponents while simultaneously guaranteeing a minimum payoff against any opponent and performing well in self-play. This new algorithm also demonstrates strong performance in empirical tests against a variety of opponents in a wide range of environments." @default.
• W82142180 created "2016-06-24" @default.
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• W82142180 date "2005-07-30" @default.
• W82142180 modified "2023-10-06" @default.
• W82142180 title "Learning against opponents with bounded memory" @default.
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