title: Decentralized learning in Markov games.
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.
authors.affiliation: Computational Modeling Laboratory (COMO), Vrije Universiteit Brussel, 1050 Brussels, Belgium.
authors.forename: Peter
authors.initials: P
authors.lastname: Vrancx
authors.forename: Katja
authors.initials: K
authors.lastname: Verbeeck
authors.forename: Ann
authors.initials: A
authors.lastname: Nowé
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D005716:Game Theory; D008390:Markov Chains; D008962:Models, Theoretical