skip to main content
10.1145/1160633.1160776acmconferencesArticle/Chapter ViewAbstractPublication PagesaamasConference Proceedingsconference-collections
Article

Lenient learners in cooperative multiagent systems

Published: 08 May 2006 Publication History

Abstract

In concurrent learning algorithms, an agent's perception of the joint search space depends on the actions currently chosen by the other agents. These perceptions change as each agent's action selection is influenced by its learning. We observe that agents that show lenience to their teammates achieve more accurate perceptions of the overall learning task. Additionally, lenience appears more beneficial at early stages of learning, when the agent's teammates are merely exploring their actions, and less helpful as the agents start to converge. We propose two multiagent learning algorithms where agents exhibit a variable degree of lenience, and we demonstrate their advantages in several coordination problems.

References

[1]
C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In Proceedings of the National Conference on Artificial Intelligence, pages 746--752, 1998.
[2]
S. Kapetanakis and D. Kudenko. Reinforcement learning of coordination in cooperative multi-agent systems. In Proceedings of the Nineteenth National Conference on Artificial Intelligence, 2002.
[3]
M. Lauer and M. Riedmiller. An algorithm for distributed reinforcement learning in cooperative multi-agent systems. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 535--542. Morgan Kaufmann, 2000.
[4]
S. Luke. ECJ 10: An Evolutionary Computation research system in Java. Available at http://www.cs.umd.edu/projects/plus/ec/ecj/, 2003.
[5]
L. Panait and S. Luke. Selecting informative actions improves cooperative multiagent learning. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi Agent Systems--AAMAS-2006, 2006.
[6]
M. Potter and K. De Jong. A cooperative coevolutionary approach to function optimization. In Y. Davidor and H.-P. Schwefel, editors, Proceedings of the Third International Conference on Parallel Problem Solving from Nature (PPSN III), pages 249--257. Springer-Verlag, 1994.
[7]
H. Schwefel. Evolution and Optimum Seeking. John Wiley and Sons, New York, 1995.
[8]
R. P. Wiegand, W. Liles, and K. De Jong. An empirical analysis of collaboration methods in cooperative coevolutionary algorithms. In E. Cantu-Paz et al, editor, Proceedings of the 2001 Genetic and Evolutionary Computation Conference (GECCO-2001), pages 1235--1242, 2001.

Cited By

View all
  • (2024)Optimistic multi-agent policy gradientProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694601(61186-61202)Online publication date: 21-Jul-2024
  • (2024)Moderate confirmation bias enhances decision-making in groups of reinforcement-learning agentsPLOS Computational Biology10.1371/journal.pcbi.101240420:9(e1012404)Online publication date: 4-Sep-2024
  • (2024)Distributed dynamic pricing of multiple perishable products using multi-agent reinforcement learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121252237:PAOnline publication date: 27-Feb-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
May 2006
1631 pages
ISBN:1595933034
DOI:10.1145/1160633
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2006

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. cooperation
  2. coordination
  3. multiagent learning

Qualifiers

  • Article

Conference

AAMAS06
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)24
  • Downloads (Last 6 weeks)2
Reflects downloads up to 19 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Optimistic multi-agent policy gradientProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694601(61186-61202)Online publication date: 21-Jul-2024
  • (2024)Moderate confirmation bias enhances decision-making in groups of reinforcement-learning agentsPLOS Computational Biology10.1371/journal.pcbi.101240420:9(e1012404)Online publication date: 4-Sep-2024
  • (2024)Distributed dynamic pricing of multiple perishable products using multi-agent reinforcement learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121252237:PAOnline publication date: 27-Feb-2024
  • (2023)Learning in Cooperative Multiagent Systems Using Cognitive and Machine ModelsACM Transactions on Autonomous and Adaptive Systems10.1145/361783518:4(1-22)Online publication date: 14-Oct-2023
  • (2023)Intrinsic fluctuations of reinforcement learning promote cooperationScientific Reports10.1038/s41598-023-27672-713:1Online publication date: 24-Jan-2023
  • (2023)Smooth Q-Learning: An Algorithm for Independent Learners in Stochastic Cooperative Markov GamesJournal of Intelligent and Robotic Systems10.1007/s10846-023-01917-z108:4Online publication date: 18-Jul-2023
  • (2023)A Relaxed Variant of Distributed Q-Learning Algorithm for Cooperative Matrix GamesArtificial Intelligence and Industrial Applications10.1007/978-3-031-43520-1_13(150-160)Online publication date: 15-Sep-2023
  • (2023)Optimistic Exploration Based on Categorical-DQN for Cooperative Markov GamesDistributed Artificial Intelligence10.1007/978-3-031-25549-6_5(60-73)Online publication date: 22-Mar-2023
  • (2022)A Dynamically Adaptive Approach to Reducing Strategic Interference for Multiagent SystemsIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2021.311095914:4(1486-1495)Online publication date: Dec-2022
  • (2022)Decentralized Learning for Optimality in Stochastic Dynamic Teams and Games With Local Control and Global State InformationIEEE Transactions on Automatic Control10.1109/TAC.2021.312122867:10(5230-5245)Online publication date: Oct-2022
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media