Abstract
In this paper we summarize some important theoretical results from the domain of Learning Automata. We start with single stage, single agent learning schema’s, and gradually extend the setting to multi-stage multi agent systems. We argue that the theory of Learning Automata is an ideal basis to build multi agent learning algorithms.
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Nowé, A., Verbeeck, K., Peeters, M. (2006). Learning Automata as a Basis for Multi Agent Reinforcement Learning. 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_3
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DOI: https://doi.org/10.1007/11691839_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33053-0
Online ISBN: 978-3-540-33059-2
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
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