Multiagent Learning in Large Anonymous Games

Autor:	Kash, Ian A., Friedman, Eric J., Halpern, Joseph Y.
Rok vydání:	2009
Předmět:	Computer Science - Multiagent Systems Computer Science - Computer Science and Game Theory Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	In large systems, it is important for agents to learn to act effectively, but sophisticated multi-agent learning algorithms generally do not scale. An alternative approach is to find restricted classes of games where simple, efficient algorithms converge. It is shown that stage learning efficiently converges to Nash equilibria in large anonymous games if best-reply dynamics converge. Two features are identified that improve convergence. First, rather than making learning more difficult, more agents are actually beneficial in many settings. Second, providing agents with statistical information about the behavior of others can significantly reduce the number of observations needed. Comment: 8 pages, 2 figures. To Appear in Proceedings of the Eighth International Conference on Autonomous Agents and Multiagent Systems
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/0903.2282 Zobrazit plný text záznamu View this record from Arxiv