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Reinforcement Learning for Stochastic Cooperative Multi-Agent Systems

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AAMAS '04: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3

Pages 1516 - 1517

Published: 19 July 2004 Publication History

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Abstract

We present a distributed variant of Q-learning that allows to learn the optimal cost-to-go function in stochastic cooperative multi-agent domains without communication between the agents.

References

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[5] M. Lauer and M. Riedmiller. An algorithm for distributed reinforcement learning in cooperative multi-agent systems. ICML, 2000.

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Cited By

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Lanctot MZambaldi VGruslys ALazaridou ATuyls KPérolat JSilver DGraepel T(2017)A unified game-theoretic approach to multiagent reinforcement learningProceedings of the 31st International Conference on Neural Information Processing Systems10.5555/3294996.3295174(4193-4206)Online publication date: 4-Dec-2017
https://dl.acm.org/doi/10.5555/3294996.3295174
Melo FLopes M(2007)Convergence of independent adaptive learnersProceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence10.5555/1782254.1782310(555-567)Online publication date: 3-Dec-2007
https://dl.acm.org/doi/10.5555/1782254.1782310
Gabel TRiedmiller M(2006)Multi-agent case-based reasoning for cooperative reinforcement learnersProceedings of the 8th European conference on Advances in Case-Based Reasoning10.1007/11805816_5(32-46)Online publication date: 4-Sep-2006
https://dl.acm.org/doi/10.1007/11805816_5

Index Terms

Reinforcement Learning for Stochastic Cooperative Multi-Agent Systems
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Cooperation and coordination
      2. Multi-agent systems
2. Mathematics of computing
  1. Probability and statistics
    1. Stochastic processes

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Published In

AAMAS '04: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3

July 2004

487 pages

ISBN:1581138644

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IEEE Computer Society

United States

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Published: 19 July 2004

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AAMAS04

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SIGAI

AAMAS04: The Third International Joint Conference on Autonomous Agents and Multi-Agent Systems 2004

July 19 - 23, 2004

New York, New York

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

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Lanctot MZambaldi VGruslys ALazaridou ATuyls KPérolat JSilver DGraepel T(2017)A unified game-theoretic approach to multiagent reinforcement learningProceedings of the 31st International Conference on Neural Information Processing Systems10.5555/3294996.3295174(4193-4206)Online publication date: 4-Dec-2017
https://dl.acm.org/doi/10.5555/3294996.3295174
Melo FLopes M(2007)Convergence of independent adaptive learnersProceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence10.5555/1782254.1782310(555-567)Online publication date: 3-Dec-2007
https://dl.acm.org/doi/10.5555/1782254.1782310
Gabel TRiedmiller M(2006)Multi-agent case-based reasoning for cooperative reinforcement learnersProceedings of the 8th European conference on Advances in Case-Based Reasoning10.1007/11805816_5(32-46)Online publication date: 4-Sep-2006
https://dl.acm.org/doi/10.1007/11805816_5

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Cooperative Multi-Agent Learning: The State of the Art

Towards reinforcement learning for holonic multi-agent systems

Training Cooperative Agents for Multi-Agent Reinforcement Learning

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Cooperative Multi-Agent Learning: The State of the Art

Towards reinforcement learning for holonic multi-agent systems

Training Cooperative Agents for Multi-Agent Reinforcement Learning

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