Abstract
This paper presents some methods of dealing with the problem of cooperative learning in a multi-agent system, in error prone environments. A system is developed that learns by reinforcement and is robust to errors that can come from the agents’ sensors, from another agent that shares wrong information or even from the communication channel.
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Sutton, R.S., Barto, A.G.: Reinforcement Learning, An Introduction. MIT Press, UK (1998)
Russel, S.J., Norvig, P.: Artificial Intelligence, A Modern Approach. Prentice Hall, New Jersey (2003)
Vlassis, N.: Multiagent Systems and Distributed AI. In: Intelligent Autonomous Systems. Informatics Institute, University of Amsterdam (2003)
Sousa, C., Custdio, L.: Cooperative Reinforcement Learning: exploring Communication and Cooperation Problems. In: Proceedings of the 6th IEEE International Symposium on Computational Intelligence in Robotics and Automation (2005)
Dietterich, T.G.: The MAXQ Method for Hierarquical Reinforcement Learning. In: International Conference on Machine Learning (1998)
Parr, R., Russel, S.: Reinforcement Learning with Hierarchies of Machines, Computer Science Division, UC Berkeley, CA (1998)
Makar, R., Mahadevan, S., Ghavamzadeh, M.: Hierarquical Multi-Agent RL, Department of Computer Science. Michigan State University (2001)
Arai, S., Sycara, K.: Credit Assignment Method for Learning Effective Stochastic Policies in Uncertain Domains. In: Proceedings of Genetic and Evolutionary Computation Conference (2001)
Arai, S., Sycara, K., Payne, T.: Experience-based RL to Acquire Effective Behaviour in a Multi-agent Domain. In: Proc. of the 6th Pacific Rim Int. Conference on Artificial Intelligence, Lecture Notes in AI 1886, pp. 125–135. Springer, Heidelberg (2000)
Arai, S., Sycara, K.: Effective Learning Approach for Planning and Scheduling n Multi-agent Domain. In: Proceedings oh the 6th ISAB - From animals to animats, pp. 507–516 (2000)
Arai, S., Sycara, K.: Multi-agent RL for Planning and Conflict Resolution in a ynamic Domain. Carnegie Mellon University (2000)
Arai, S., Sycara, K., Payne, T.: Multi-agent Reinforcement Learning for Planning nd Scheduling Multiple Goals. In: Proceedings of Fourth International Conference n Multi-Agent Systems (2000)
Wahab, M.: Reinforcement Learning in Multi-Agent Systems. McGill Univ. School f Computer Science
Ghavamzadeh, M., Mahadevan, S.: Learning to Communicate and Act in Cooperative ultiagent Systems using Hierarchical Reinforcement Learning. In: Third nternational Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004, vol. 3, pp. 1114–1121 (2004)
Tan, M.: Multi-Agent RL: Independent vs. Cooperative Agents. In: Proceedings of he Tenth International Conference on Machine Learning, pp. 330–337 (1993)
Bowling, M., Veloso, M.: An Analysis of Stochastic Game Theory for Multiagent einforcement Learning, CMU-CS-00-165 (2000)
Bowling, M.: Multiagent Learning in the Presence of Agents with Limitations, PhD. Thesis, Carnegie Mellon University, Pittsburg (2003)
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© 2006 Springer-Verlag Berlin Heidelberg
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Sousa, C.O.e., Custódio, L. (2006). Dealing with Errors in a Cooperative Multi-agent Learning System. In: Tuyls, K., Hoen, P.J., Verbeeck, K., Sen, S. (eds) Learning and Adaption in Multi-Agent Systems. LAMAS 2005. Lecture Notes in Computer Science(), vol 3898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11691839_8
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DOI: https://doi.org/10.1007/11691839_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33053-0
Online ISBN: 978-3-540-33059-2
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