Abstract
In this paper we revise Reinforcement Learning and adaptiveness in Multi-Agent Systems from an Evolutionary Game Theoretic perspective. More precisely we show there is a triangular relation between the fields of Multi-Agent Systems, Reinforcement Learning and Evolutionary Game Theory. We illustrate how these new insights can contribute to a better understanding of learning in MAS and to new improved learning algorithms. All three fields are introduced in a self-contained manner. Each relation is discussed in detail with the necessary background information to understand it, along with major references to relevant work.
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References
Bazzan A. L. C. and Franziska Klugl: 2003, ‘Learning to Behave Socially and Avoid the Braess Paradox in a Commuting Scenario’, in Proceedings of the First International Workshop on Evolutionary Game Theory for Learning in MAS, Melbourne Australia.
Bazzan A. L. C.: 1997, A Game-Theoretic Approach to Coordination of Traffic Signal Agents, Ph. D. thesis, University of Karlsruhe.
Börgers, T. and R. Sarin: 1997, ‘Learning through Reinforcement and Replicator Dynamics’, Journal of Economic Theory 77(1).
Braess D.: 1968, ‘Uber ein paradoxon aus der verkehrsplanung’, Unternehmensforschung 12, 258.
Bush, R. R. and F. Mosteller, F.: 1955, Stochastic Models for Learning, Wiley, New York.
Claus, C. and C. Boutilier: 1998, ‘The Dynamics of Reinforcement Learning in Cooperative Multi-Agent Systems, in Proceedings of the 15th International Conference on Artificial Intelligence, pp. 746–752.
Ghosh, A. and S. Sen: 2003, ‘Learning TOMs: Convergence to Non-Myopic Equilibria’, in Proceedings of the First International Workshop on Evolutionary Game Theory for Learning in MAS, Melbourne, Australia.
Gintis, C. M.: 2000, Game Theory Evolving, University Press, Princeton.
Hirsch, M. W. and S. Smale: 1974, Differential Equations, Dynamical Systems and Linear Algebra, Academic Press, Inc.
Hofbauer, J. and K. Sigmund: 1998, Evolutionary Games and Population Dynamics, Cambridge University Press.
Hu, J. and M. P. Wellman: 1998, Multiagent Reinforcement Learning in Stochastic Games, Cambridge University Press.
Jafari, C., A. Greenwald, D. Gondek, and G. Ercal: 2001, ‘On No-Regret Learning, Fictitious Play, and Nash Equilibrium’, in Proceedings of the Eighteenth International Conference on Machine Learning, pp. 223–226.
Kaelbling, L. P., M. L. Littman, and A. W. Moore: 1996, ‘Reinforcement Learning: A Survey’, Journal of Artificial Intelligence Research.
Littman, M. L.: 1994, ‘Markov Games as a Framework for Multi-Agent Reinforcement Learning’, Proceedings of the Eleventh International Conference on Machine Learning, pp. 157–163.
Loch, J. and S. Singh: 1998, ‘Using Eligibility Traces to Find the Best Memoryless Policy in a Partially Observable Markov Process’, Proceedings of the Fifteenth International Conference on Machine Learning, San Francisco.
Luck, M., P. McBurney, and C. Preist: 2003, ‘A Roadmap for Agent Based Computing’, AgentLink, Network of Excellence.
Maynard-Smith, J.: 1982, Evolution and the Theory of Games, Cambridge University Press.
Maynard Smith, J. and G. R. Price: 1973, ‘The Logic of Animal Conflict’, Nature 146, 15–18.
Narendra, K. and M. Thathachar: 1989, Learning Automata: An Introduction, Prentice-Hall.
Nowé, A., J. Parent, and K. Verbeeck: 2001, ‘Social Agents Playing a Periodical Policy’, in Proceedings of the 12th European Conference on Machine Learning, pp. 382–393.
Nowé A. and K. Verbeeck: 1999, ‘Distributed Reinforcement learning, Loadbased Routing a Case Study’, Notes of the Neural, Symbolic and Reinforcement Methods for Sequence Learning Workshop at ijcai99, Stockholm, Sweden.
von Neumann, J. and O. Morgenstern: 1944, Theory of Games and Economic Behaviour, Princeton University Press, Princeton.
Osborne, J. O. and A. Rubinstein: 1994, A Course in Game Theory, MIT Press, Cambridge, MA.
Pendrith, M. D. and M. J. McGarity: 1998, ‘An Analysis of Direct Reinforcement Learning in Non-Markovian Domains’, in Proceedings of the Fifteenth International Conference on Machine Learning, San Francisco.
Perkins, T. J. and M. D. Pendrith: 2002, ‘On the Existence of Fixed Points for Q-Learning and Sarsa in Partially Observable Domains’, in Proceedings of the International Conference on Machine Learning (ICML02).
Redondo, F. V.: 2001, Game Theory and Economics, Cambridge University Press.
Robocup project: 2003, ‘The Official Robocup Website at www.robocup.org, Robocup.
Samuelson, L.: 1997, Evolutionary Games and Equilibrium Selection, MIT Press, Cambridge, MA.
Schneider, T. D.: 2000, ‘Evolution of Biological Information’, Journal of Nucleic Acids Research 28, 2794–2799.
Stauffer, D.: 1999, Life, Love and Death: Models of Biological Reproduction and Aging, Institute for Theoretical Physics, Köln, Euroland.
Stone P.: 2000, Layered Learning in Multi-Agent Systems, MIT Press, Cambridge, MA.
Sutton, R. S. and A. G. Barto: 1998, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA.
Tsitsiklis, J. N.: 1993, ‘Asynchronous Stochastic Approximation and q-Learning’, Internal Report from the Laboratory for Information and Decision Systems and the Operation Research Center, MIT Press, Cambridge, MA.
Tuyls, K., T. Lenaerts, K. Verbeeck, S. Maes, and B. Manderick: 2002, ‘Towards a Relation between Learning Agents and Evolutionary Dynamics’, in Proceedings of the Belgium-Netherlands Artificial Intelligence Conference 2002 (BNAIC), KU Leuven, Belgium.
Tuyls, K., K. Verbeeck, and S. Maes: 2003a, ‘On a Dynamical Analysis of Reinforcement Learning in Games: Emergence of Occam’s Razor, Lecture Notes in Artificial Intelligence, Multi-Agent Systems and Applications III, Lecture Notes in AI 2691, (Central and Eastern European conference on Multi-Agent Systems 2003), Prague, 16–18 June 2003, Czech Republic.
Tuyls, K., K. Verbeeck, and T. Lenaerts, T.: 2003b, ‘A Selection-Mutation Model for QLearning in Multi-Agent Systems’, in The ACM International Conference Proceedings Series, Autonomous Agents and Multi-Agent Systems 2003, Melbourne, 14–18 July 2003, Australia.
Tuyls, K., D. Heytens, A. Nowe, and B. Manderick: 2003c, ‘Extended Replicator Dynamics as a Key to Reinforcement Learning in Multi-Agent Systems’, Proceedings of the European Conference on Machine Learning’ 03, Lecture Notes in Artificial Intelligence, Cavtat-Dubrovnik, 22–26 September 2003, Croatia.
Weibull, J. W.: 1996, Evolutionary Game Theory, MIT Press, Cambridge, MA.
Weibull, J. W.: 1998, ‘What we have Learned from Evolutionary Game Theory so Far?’, Stockholm School of Economics and I.U.I., May 7, 1998.
Weiss, G.: 1999, in Gerard Weiss (ed.), Multiagent Systems. A Modern Approach to Distributed Artificial Intelligence, MIT Press, Cambridge, MA.
Wooldridge, M.: 2002, An Introduction to MultiAgent Systems, John Wiley & Sons, Chichester, England.
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Tuyls, K., Nowe, A., Lenaerts, T., Manderick, B. (2004). An Evolutionary Game Theoretic Perspective on Learning in Mult-Agent Systems. In: Information, Interaction and Agency. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4094-6_5
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DOI: https://doi.org/10.1007/1-4020-4094-6_5
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3600-2
Online ISBN: 978-1-4020-4094-8
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