General principles of learning-based multi-agent systems
David H. Wolpert, Kevin Wheeler, Kagan Tumer · 1999
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 don 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 [l] (and in psrtitular avoid the tragedy of the commons for that problem), and we also illustrate optimal performance for our COINS in the leader-follower problem.