Multi-agent learning model with bargaining

Haiyan Qiao, Jerzy Rozenblit, Ferenc Szidarovszky, Lizhi Yang

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

13 Scopus citations


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
Number of pages7
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


Other2006 Winter Simulation Conference, WSC
Country/TerritoryUnited States
CityMonterey, CA

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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