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2008

Decentralized Learning in Markov Games

8 years 6 months ago
Decentralized Learning in Markov Games
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. In this paper we extend this result to the framework of Markov Games, a straightforward extension of single-agent Markov Decision Problems to distributed multi-agent decision problems. We put a simple learning automaton for every agent in each state and show that the problem can be viewed from 3 different perspectives; the single superagent view, in which a single agent is represented by the whole set of automata, the multi-agent view, in which each agent is represented by the automata it was associated with in each state and finally the LA-view, i.e. the view in which each automaton itself represents an agent. We show that under the same ergodic assumptions of th...
Peter Vrancx, Katja Verbeeck, Ann Nowé
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TSMC
Authors Peter Vrancx, Katja Verbeeck, Ann Nowé
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