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General Principles of Learning-Based Multi-Agent Systems

David H. Wolpert·Kevin R. Wheeler·Kagan Tumer
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Abstract

We consider the problem of how to design large decentralized multi-agent systems (MAS's) in an automated fashion, with little or no hand-tuning. Our approach has each agent run a reinforcement learning algorithm. This converts the problem into one of how to automatically set/update the reward functions for each of the agents so that the global goal is achieved. In particular we do not want the agents to ``work at cross-purposes'' as far as the global goal is concerned. We use the term artificial COllective INtelligence (COIN) to refer to systems that embody solutions to this problem. In this paper we present a summary of a mathematical framework for COINs. We then investigate the real-world applicability of the core concepts of that framework via two computer experiments: we show that our COINs perform near optimally in a difficult variant of Arthur's bar problem (and in particular avoid the tragedy of the commons for that problem), and we also illustrate optimal performance for our COINs in the leader-follower problem.

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David H. Wolpert, Kevin R. Wheeler, Kagan Tumer. 1999-05-10. General Principles of Learning-Based Multi-Agent Systems. https://arxiv.org/abs/cs/9905005

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