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Decentralized Learning in Markov Games

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3 Author(s)
Vrancx, P. ; Comput. Modeling Lab., Vrije Univ. Brussel, Brussels ; Verbeeck, K. ; Nowe, A.

Learning automata (LA) were recently shown to be valuable tools for designing multiagent reinforcement learning algorithms. One of the principal contributions of the LA theory is that a set of decentralized independent LA is able to control a finite Markov chain with unknown transition probabilities and rewards. In this paper, we propose to extend this algorithm to Markov games-a straightforward extension of single-agent Markov decision problems to distributed multiagent decision problems. We show that under the same ergodic assumptions of the original theorem, the extended algorithm will converge to a pure equilibrium point between agent policies.

Published in:

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:38 ,  Issue: 4 )

Date of Publication:

Aug. 2008

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