Multi-Agent Reinforcement Learning:a critical survey
Yoav Shoham, Rob Powers, Trond Grenager · 2003
We survey the recent work in AI on multi-agent reinforcement learning (that is, learning in stochastic games). We then argue that, while exciting, this work is flawed. The fundamental flaw is unclarity about the problem or problems being addressed. After tracing a representative sample of the recent literature, we identify four well-defined problems in multi-agent reinforcement learning, single out the problem that in our view is most suitable for AI, and make some remarks about how we believe progress is tobemadeonthisproblem. 1