Incremental policy learning: An equilibrium selection algorithm for reinforcement learning agents with common interests

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 2, Issue , 2004, Pages 1121-1125

  Fulda, Nancy ^a   Ventura, Dan ^a  

^a 3361 TMCB   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INCREMENTAL POLICY LEARNING (IPL); INFORMATION PROCESSING SYSTEMS; LEARNING AGENTS; REINFORCEMENT LEARNING;

ALGORITHMS; HEURISTIC METHODS; INFORMATION RETRIEVAL; LEARNING ALGORITHMS; LEARNING SYSTEMS; OPTIMIZATION; PARAMETER ESTIMATION; PROBABILITY; PROBLEM SOLVING;

MULTI AGENT SYSTEMS;

EID: 10944261341     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2004.1380091     Document Type: Conference Paper

References (9)

1. Caroline Claus and Craig Boutilier. The dynamics of reinforcement learning in cooperative multiagent systems. In AAAI/IAAI, pages 746-752, 1998.
2. H. Jurgens H. O. Peitgen and D. Supe. Chaos and Fractals. Springer-Verlag, New York, 1992.
3. J. Hu and M. Wellman. Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research, to appear, 2003.
4. S. Kapetanakis and D. Kudenko. Improving on the reinforcement learning of coordination in cooperative multi-agent systems. In Second AISB Symposium on Adaptive Agents and Multi-Agent Systems, 2002.
5. Martin Lauer and Martin Riedmiller. An algorithm for distributed reinforcement learning in cooperative multi-agent systems. In Proceedings of the 17th International Conference on Machine Learning, pages 535-542, San Francisco, CA, 2000. Morgan Kaufman.
6. Michael Littman. Friend or foe q-learning in general-sum markov games. In Proceedings of the Eighteenth International Conference on Machine Learning, pages 322-328, 2001.
7. H. A. Simon. A behavioral model of rational choice.
8. W. C. Stirling, M. A. Goodrich, and D. J. Packard. Satisficing equilibria: A non-classical approach to games and decisions. Autonomous Agents and Multi-Agent Systems Journal, 5:305-328, 2002.
9. X. Wang and T. Sandholm. Reinforcement learning to play an optimal nash equilibrium in team markov games. In Advances in Neural Information Processing Systems 15 (NIPS-2002), Vancouver, 2002.