Abstract
DEC-MDPs with changing action sets and partially ordered transition dependencies have recently been suggested as a sub-class of general DEC-MDPs that features provably lower complexity. In this paper, we investigate the usability of a coordinated batch-mode reinforcement learning algorithm for this class of distributed problems. Our agents acquire their local policies independent of the other agents by repeated interaction with the DEC-MDP and concurrent evolvement of their policies, where the learning approach employed builds upon a specialized variant of a neural fitted Q iteration algorithm, enhanced for use in multi-agent settings. We applied our learning approach to various scheduling benchmark problems and obtained encouraging results that show that problems of current standards of difficulty can very well approximately, and in some cases optimally be solved.
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References
Beasley, J.: OR-Library (2005), http://people.brunel.ac.uk/~mastjjb/jeb/info.html
Becker, R., Zilberstein, S., Lesser, V.: Decentralized Markov Decision Processes with Event-Driven Interactions. In: Proceedings of AAMAS 2004, pp. 302–309. ACM Press, New York (2004)
Becker, R., Zilberstein, S., Lesser, V., Goldman, C.: Solving Transition Independent Decentralized MDPs. Journal of AI Research 22, 423–455 (2004)
Bernstein, D., Givan, D., Immerman, N., Zilberstein, S.: The Complexity of Decentralized Control of Markov Decision Processes. Mathematics of Operations Research 27(4), 819–840 (2002)
Boutilier, C.: Sequential Optimality and Coordination in Multiagent Systems. In: Proceedings of IJCAI 1999, Sweden, pp. 478–485. Morgan Kaufmann, San Francisco (1999)
Brafman, R., Tennenholtz, M.: Learning to Cooperate Efficiently: A Model-Based Approach. Journal of AI Research 19, 11–23 (2003)
Buffet, O., Dutech, A., Charpillet, F.: Shaping Multi-Agent Systems with Gradient Reinforcement Learning. Autonomous Agent and Multi-Agent System Journal 15(2), 197–220 (2007)
Ernst, D., Geurts, P., Wehenkel, L.: Tree-Based Batch Mode Reinforcement Learning. Journal of Machine Learning Research (6), 504–556 (2005)
Gabel, T., Riedmiller, M.: Adaptive Reactive Job-Shop Scheduling with Learning Agents. International Journal of Information Technology and Intelligent Computing 2(4) (2007)
Gabel, T., Riedmiller, M.: Reinforcement Learning for DEC-MDPs with Changing Action Sets and Partially Ordered Dependencies. In: Proceedings of AAMAS 2008, Estoril, Portugal, pp. 1333–1336. IFAAMAS (2008)
Goldman, C., Zilberstein, S.: Optimizing Information Exchange in Cooperative Multi-Agent Systems. In: Proceedings of AAMAS 2003, Melbourne, Australia, pp. 137–144. ACM Press, New York (2003)
Lauer, M., Riedmiller, M.: An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems. In: Proceedings of ICML 2000, Stanford, USA, pp. 535–542. AAAI Press, Menlo Park (2000)
Pinedo, M.: Scheduling. Theory, Algorithms, and Systems. Prentice Hall, Englewood Cliffs (2002)
Riedmiller, M.: Neural Fitted Q Iteration – First Experiences with a Data Efficient Neural Reinforcement Learning Method. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS, vol. 3720, pp. 317–328. Springer, Heidelberg (2005)
Riedmiller, M., Braun, H.: A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. In: Ruspini, H. (ed.) Proceedings of ICNN, San Francisco, USA, pp. 586–591 (1993)
Szer, D., Charpillet, F.: Coordination through Mutual Notification in Cooperative Multiagent RL. In: Proceedings of AAMAS 2004, pp. 1254–1255. IEEE Computer Society, Los Alamitos (2005)
Verbeeck, K., Nowe, A., Tuyls, K.: Coordinated Exploration in Multi-Agent Reinforcement Learning: An Application to Load-Balancing. In: Proceedings of AAMAS 2005, Utrecht, The Netherlands, pp. 1105–1106. ACM Press, New York (2005)
Watkins, C., Dayan, P.: Q-Learning. Machine Learning 8, 279–292 (1992)
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Gabel, T., Riedmiller, M. (2008). Evaluation of Batch-Mode Reinforcement Learning Methods for Solving DEC-MDPs with Changing Action Sets. In: Girgin, S., Loth, M., Munos, R., Preux, P., Ryabko, D. (eds) Recent Advances in Reinforcement Learning. EWRL 2008. Lecture Notes in Computer Science(), vol 5323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89722-4_7
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DOI: https://doi.org/10.1007/978-3-540-89722-4_7
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