Performance Bounded Reinforcement Learning in Strategic Interactions

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Despite increasing deployment of agent technologies in several business and industry domains, user confidence in fully automated agent driven applications is noticeably lacking. The main reasons for such lack of trust in complete automation are scalability and non-existence of reasonable guarantees in the performance of self-adapting software. In this paper we address the latter issue in the context of learning agents in a Multiagent System (MAS). Performance guarantees for most existing on-line Multiagent Learning (MAL) algorithms are realizable only in the limit, thereby seriously limiting its practical utility. Our goal is to provide certain meaningful guarantees about the performance of a learner in a MAS, while it is learning. In particular, we present a novel MAL algorithm that (i) converges to a best response against stationary opponents, (ii) converges to a Nash equilibrium in self-play and (iii) achieves a constant bounded expected regret at any time (no-average-regret asymptotically) in arbitrary sized general-sum games with non-negative payoffs, and against any number of opponents.

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