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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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