Abstract
In this paper we report on a solution method for one of the most challenging problems in Multi-agent Reinforcement Learning, i.e. coordination. In previous work we reported on a new coordinated exploration technique for individual reinforcement learners, called Exploring Selfish Reinforcement Rearning (ESRL). With this technique, agents may exclude one or more actions from their private action space, so as to coordinate their exploration in a shrinking joint action space. Recently we adapted our solution mechanism to work in tree structured common interest multi-stage games. This paper is a roundup on the results for stochastic single and multi-stage common interest games.
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References
Boutilier, C.: Sequential optimality and coordination in multiagent systems. In: Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, Sweden, pp. 478–485 (1999)
Carpenter, M., Kudenko, D.: Baselines for joint-action reinforcement learning of coordination in cooperative multi-agent systems. In: Kudenko, D., Kazakov, D., Alonso, E. (eds.) AAMAS 2004. LNCS, vol. 3394, pp. 55–72. Springer, Heidelberg (2005)
Claus, C., Boutilier, C.: The dynamics of reinforcement learning in cooperative multiagent systems. In: Proceedings of the fiftheenth National Conference on Artificial Intelligence, pp. 746–752 (1998)
Hu, J., Wellman, M.P.: Nash q-learning for general-sum stochastic games. Journal of Machine Learning Research 4, 1039–1069 (2003)
Kapetanakis, S., Kudenko, D., Strens, M.: Learning to coordinate using commitment sequences in cooperative multi-agent systems. In: Kudenko, D., Kazakov, D., Alonso, E. (eds.) AAMAS 2004. LNCS, vol. 3394, pp. 106–118. Springer, Heidelberg (2005)
Lauer, M., Riedmiller, M.: An algorithm for distributed reinforcement learning in cooperative multi-agent systems. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 535–542 (2000)
Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 322–328 (2001)
Narendra, K.S., Parthasarathy, K.: Learning automata approach to hierarchical multiobjective analysis. Technical Report No. 8811, Electrical Engineering. Yale University., New Haven, Connecticut (1988)
Narendra, K.S., Thathachar, M.A.L.: Learning Automata: An Introduction. Prentice-Hall International, Inc., Englewood Cliffs (1989)
Nowé, A., Parent, J., Verbeeck, K.: Social agents playing a periodical policy. In: Proceedings of the 12th European Conference on Machine Learning, Freiburg, Germany. LNCS (LNAI), vol. 2168, pp. 382–393. Springer, Heidelberg (2001)
Osborne, J.O., Rubinstein, A.: A course in game theory. MIT Press, Cambridge (1994)
Parent, J., Verbeeck, K., Nowe, A., Steenhaut, K., Lemeire, J., Dirkx, E.: Adaptive load balancing of parallel applications with social reinforcement learning on heterogeneous systems. Scientific Programming (2004) (to appear)
Peeters, M., Verbeeck, K., Nowé, A.: Multi-agent learning in conflicting multi-level games with incomplete information. In: Proceedings of the 2004 American Association for Artificial Intelligence (AAAI) Fall Symposium on Artificial Multi-Agent Learning (2004)
Hoen, P.J.’t., Tuyls, K.: Analyzing multi-agent reinforcement learning using evolutionary dynamics. In: Boulicaut, J.-F., et al. (eds.) ECML 2004. LNCS, vol. 3201, pp. 168–179. Springer, Heidelberg (2004)
Thathachar, M.A.L., Sastry, P.S.: Varieties of learning automata: An overview. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 32(6), 711–722 (2002)
Tuyls, K., Nowe, A., Lenaerts, T., Manderick, B.: An evolutionary game theoretic perspective on learning in multi-agent systems. Synthese, section Knowledge, Rationality and Action 139(2), 297–330 (2004)
Verbeeck, K., Nowé, A., Parent, J., Tuyls, K.: Exploring selfish reinforcement learning in non-zero sum games (2004) (submitted)
Verbeeck, K., Nowé, A., Peeters, M.: Multi-agent coordination in tree structured multi-stage games. In: Proceedings of the Fourth Symposium on Adaptive Agents and Multi-agent Systems (AISB 2004) Society for the study of Artificial Intelligence and Simulation of Behaviour, pp. 63–74 (2004)
Verbeeck, K., Nowé, A., Tuyls, K.: Coordinated exploration in stochastic common interest games. In: Proceedings of the Third Symposium on Adaptive Agents and Multi-agent Systems (AISB 2003) Society for the study of Artificial Intelligence and Simulation of Behaviour (2003)
Wolpert, D.H., Wheller, K.R., Tumer, K.: General principles of learning-based multi-agent systems. In: Etzioni, O., Müller, J.P., Bradshaw, J.M. (eds.) Proceedings of the Third International Conference on Autonomous Agents (Agents 1999), Seattle, WA, USA, pp. 77–83. ACM Press, New York (1999)
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Verbeeck, K., Nowé, A., Peeters, M., Tuyls, K. (2005). Multi-agent Reinforcement Learning in Stochastic Single and Multi-stage Games. In: Kudenko, D., Kazakov, D., Alonso, E. (eds) Adaptive Agents and Multi-Agent Systems II. AAMAS AAMAS 2004 2003. Lecture Notes in Computer Science(), vol 3394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32274-0_18
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DOI: https://doi.org/10.1007/978-3-540-32274-0_18
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