Performance Bounded Reinforcement Learning in Strategic Interactions

11 years 2 months ago
Performance Bounded Reinforcement Learning in Strategic Interactions
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 selfadapting 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-averageregret asymptot...
Bikramjit Banerjee, Jing Peng
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where AAAI
Authors Bikramjit Banerjee, Jing Peng
Comments (0)