General Principles of Learning-Based Multi-Agent Systems

We consider the problem of how to design large decentralized multiagent 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 [1] (and in particular avoid the tragedy of the commons for that problem), and we also illustrate optimal performanc...

• Type: Conference
• Year: 1999
• Where: AGENTS
• Authors: David Wolpert, Kevin R. Wheeler, Kagan Tumer
• Keywords: AGENTS 1999, Global Goal, Large Decentralized Multiagent, Reinforcement Learning Algorithm, Security Privacy