Title: Decentralized learning in Markov games
Authors: Vrancx, Peter ×
Verbeeck, Katja
Nowe, Ann #
Issue Date: Aug-2008
Publisher: Institute of Electrical and Electronics Engineers
Series Title: IEEE Transactions on Systems, Man and Cybernetics B, Cybernetics vol:38 issue:4 pages:976-981
Abstract: 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.
ISSN: 1083-4419
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Non-KU Leuven Association publications
× corresponding author
# (joint) last author

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