General Principles of Learning-Based Multi-Agent Systems
<|reference_start|>General Principles of Learning-Based Multi-Agent Systems: 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.<|reference_end|>
@article{wolpert1999general,
    title={General Principles of Learning-Based Multi-Agent Systems},
    author={David H. Wolpert, Kevin R. Wheeler, Kagan Tumer},
    journal={Proceedings of the Third International Conference on Autonomous
  Agents, Seatle, WA 1999},
    year={1999},
    archivePrefix={arXiv},
    eprint={cs/9905005},
    primaryClass={cs.MA adap-org cond-mat.stat-mech cs.DC cs.LG nlin.AO}
}