Multi-agent learning model with bargaining

Haiyan Qiao, Jerzy Rozenblit, Ferenc Szidarovszky, Lizhi Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Decision problems with the features of prisoner's dilemma are quite common. A general solution to this kind of social dilemma is that the agents cooperate to play a joint action. The Nash bargaining solution is an attractive approach to such cooperative games. In this paper, a multi-agent learning algorithm based on the Nash bargaining solution is presented. Different experiments are conducted on a testbed of stochastic games. The experimental results demonstrate that the algorithm converges to the policies of the Nash bargaining solution. Compared with the learning algorithms based on a non-cooperative equilibrium, this algorithm is fast and its complexity is linear with respect to the number of agents and number of iterations. In addition, it avoids the disturbing problem of equilibrium selection.

Original languageEnglish (US)
Title of host publicationProceedings of the 2006 Winter Simulation Conference, WSC
Pages934-940
Number of pages7
DOIs
StatePublished - 2006
Event2006 Winter Simulation Conference, WSC - Monterey, CA, United States
Duration: Dec 3 2006Dec 6 2006

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736

Other

Other2006 Winter Simulation Conference, WSC
CountryUnited States
CityMonterey, CA
Period12/3/0612/6/06

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Multi-agent learning model with bargaining'. Together they form a unique fingerprint.

  • Cite this

    Qiao, H., Rozenblit, J., Szidarovszky, F., & Yang, L. (2006). Multi-agent learning model with bargaining. In Proceedings of the 2006 Winter Simulation Conference, WSC (pp. 934-940). [4117702] (Proceedings - Winter Simulation Conference). https://doi.org/10.1109/WSC.2006.323178