Article

Multi-agent learning model with bargaining

Authors:

Jerzy Rozenblit,

Ferenc Szidarovszky,

Lizhi YangAuthors Info & Claims

WSC '06: Proceedings of the 38th conference on Winter simulation

Pages 934 - 940

Published: 03 December 2006 Publication History

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.

References

[1]

Banerjee, B., and J. Peng. 2005. Efficient No-Regret Multiagent Learning. In Twenty National Conference on Artificial Intelligence (AAAI 2005), 41--46.

Digital Library

[2]

Bowling, M. and M. Veloso. 2002. Multiagent learning using a variable learning rate. Artificial Intelligence 136: 215--250.

Digital Library

[3]

Claus, C., and C. Boutilier. 1998. The dynamics of reinforcement learning in cooperative multiagent systems. In Fifteen National Conference on Artificial Intelligence, 746--752. Madison, WI.

Digital Library

[4]

Conitzer, V. and T. Sandholm. 2003. AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents. In Proceedings of the twentieth International Conference on Machine Learning (ICML 2003), 83--90. Washington, DC.

[5]

Greenwald, A. and K. Hall. 2003. Correlated Q-learning. In Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), 242--249. Washington DC.

[6]

Hu, J. and M. P. Wellman. 1998. Multiagent reinforcement learning: Theoretical framework and an algorithm. In Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), 242--250. Madison, WI.

Digital Library

[7]

Hu, J. and M. P. Wellman. 2003. Nash Q-learning for general-sum stochastic games, Journal of Machine Learning Research 4:1039--1069.

[8]

Littman, M. L. 1994. Markov games as a framework for multi-agent reinforcement learning. In Eleventh International Conference on Machine Learning (ICML 1994), 157--163. New Brunswick, NJ.

Digital Library

[9]

Littman, M. L. and P. Stone. 2001. Implicit Negotiation in Repeated Games. In Proceedings of the Eighth International Workshop on Agent Theories, Architectures, and Languages, 393--404.

Digital Library

[10]

Nash, J. F. 1953. Two-person Cooperative games. Econometrica 21:128--140.

[11]

Powers, R, Y. Shoham. 2005. New criteria and a new algorithm for learning in multi-agent systems. In Nineteenth International Joint Conference on Artificial Intelligence (IJCAI 2005), 817--822. Edinburgh, Scotland.

[12]

Shoham, Y., R. Powers, and T. Grenager. 2003. Multi-Agent Reinforcement Learning: a critical survey. Technical Report

[13]

Stimpson, J. L. and M. A. Goodrich. 2003a. Learning To Cooperate in a Social Dilemma: A Satisficing Approach to Bargaining. In Twentieth International Conference on Machine Learning (ICML 2003): 728--735. Washington DC.

[14]

Stimpson, J. L., and M. A. Goodrich. 2003b. Nash Equilibrium or Nash Bargaining? Choosing a Solution Concept for Multi-Agent Learning. Proceedings of the 2003 AAMAS Workshop on Game Theoretic and Decision Theoretic Agents. Melbourne, Australia, July, 2003.

[15]

Sutton, R. S. and A. G. Barto. 1998. Reinforcement Learning: An introduction. MIT press.

Digital Library

Cited By

Crandall Jvan der Hoek WPadgham LConitzer VWinikoff M(2012)Just add PepperProceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 110.5555/2343576.2343633(399-406)Online publication date: 4-Jun-2012
https://dl.acm.org/doi/10.5555/2343576.2343633

Multi-agent learning model with bargaining
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence

Recommendations

Mediated Multi-Agent Reinforcement Learning
AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems

The majority of Multi-Agent Reinforcement Learning (MARL) literature equates the cooperation of self-interested agents in mixed environments to the problem of social welfare maximization, allowing agents to arbitrarily share rewards and private ...
Using temporal-difference learning for multi-agent bargaining

This research treats a bargaining process as a Markov decision process, in which a bargaining agent's goal is to learn the optimal policy that maximizes the total rewards it receives over the process. Reinforcement learning is an effective method for ...
Decentralized anti-coordination through multi-agent learning

To achieve an optimal outcome in many situations, agents need to choose distinct actions from one another. This is the case notably in many resource allocation problems, where a single resource can only be used by one agent at a time. How shall a ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WSC '06: Proceedings of the 38th conference on Winter simulation

December 2006

2429 pages

ISBN:1424405017

Editors:
L. Felipe Perrone
Bucknell University
,
Barry G. Lawson
University of Richmond
,
Jason Liu
Colorado School of Mines
,
Frederick P. Wieland
Sensis Corporation
,
General Chair:
David Nicol
University of Illinois, Urbana-Champaign
,
Program Chair:
Richard Fujimoto
College of Computing, Georgia Tech

Sponsors

IIE: Institute of Industrial Engineers
ASA: American Statistical Association
IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
IEEE-CS\DATC: The IEEE Computer Society
SIGSIM: ACM Special Interest Group on Simulation and Modeling
NIST: National Institute of Standards and Technology
(SCS): The Society for Modeling and Simulation International
INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation

Publisher

Winter Simulation Conference

Publication History

Published: 03 December 2006

Check for updates

Qualifiers

Article

Conference

WSC06

Sponsor:

IIE
ASA
IEICE ESS
IEEE-CS\DATC
SIGSIM
NIST
(SCS)
INFORMS-CS

WSC06: Winter Simulation Conference 2006

December 3 - 6, 2006

California, Monterey

Acceptance Rates

WSC '06 Paper Acceptance Rate 177 of 252 submissions, 70%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
294
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 22 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Crandall Jvan der Hoek WPadgham LConitzer VWinikoff M(2012)Just add PepperProceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 110.5555/2343576.2343633(399-406)Online publication date: 4-Jun-2012
https://dl.acm.org/doi/10.5555/2343576.2343633

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents