Learning automata as a basis for multi agent reinforcement learning

Authors:
Ann Nowé;Katja Verbeeck;Maarten Peeters
Affiliations:
Computational Modeling Lab, Vrije Universiteit Brussel, Brussel, Belgium;Computational Modeling Lab, Vrije Universiteit Brussel, Brussel, Belgium;Computational Modeling Lab, Vrije Universiteit Brussel, Brussel, Belgium
Venue:
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Year:
2005

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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.