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Conference Publication

Optimal Convergence in Multi-Agent MDPs

Host Publication: Knowledge-Based Intelligent Information and Engineering Systems (KES 2007)

Authors: P. Vrancx, K. Verbeeck and A. Nowé

Publisher: Springer

Publication Year: 2007

Number of Pages: 8

ISBN: 3-540-74828-8


Abstract:

Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is that a set of decentralized, independent learning automata is able to control a finite Markov Chain with unknown transition probabilities and rewards. We extend this result to the framework of Multi-Agent MDP's, a straightforward extension of single-agent MDP's to distributed cooperative multi-agent decision problems. Furthermore, we combine this result with the application of parametrized learning automata yielding global optimal convergence results.

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