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Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems

Published online by Cambridge University Press:  22 February 2012

Laetitia Matignon ,
Guillaume J. Laurent  and
Nadine Le Fort-Piat
Laetitia Matignon*
Affiliation:
FEMTO-ST Institute, UMR CNRS 6174, UFC/ENSMM/UTBM, 24 rue Alain Savary, 25000 Besançon, France
Guillaume J. Laurent*
Affiliation:
FEMTO-ST Institute, UMR CNRS 6174, UFC/ENSMM/UTBM, 24 rue Alain Savary, 25000 Besançon, France
Nadine Le Fort-Piat*
Affiliation:
FEMTO-ST Institute, UMR CNRS 6174, UFC/ENSMM/UTBM, 24 rue Alain Savary, 25000 Besançon, France
*
e-mail: laetitia.matignon@gmail.com, guillaume.laurent@ens2m.fr, nadine.piat@ens2m.fr
e-mail: laetitia.matignon@gmail.com, guillaume.laurent@ens2m.fr, nadine.piat@ens2m.fr
e-mail: laetitia.matignon@gmail.com, guillaume.laurent@ens2m.fr, nadine.piat@ens2m.fr
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Abstract

In the framework of fully cooperative multi-agent systems, independent (non-communicative) agents that learn by reinforcement must overcome several difficulties to manage to coordinate. This paper identifies several challenges responsible for the non-coordination of independent agents: Pareto-selection, non-stationarity, stochasticity, alter-exploration and shadowed equilibria. A selection of multi-agent domains is classified according to those challenges: matrix games, Boutilier's coordination game, predators pursuit domains and a special multi-state game. Moreover, the performance of a range of algorithms for independent reinforcement learners is evaluated empirically. Those algorithms are Q-learning variants: decentralized Q-learning, distributed Q-learning, hysteretic Q-learning, recursive frequency maximum Q-value and win-or-learn fast policy hill climbing. An overview of the learning algorithms’ strengths and weaknesses against each challenge concludes the paper and can serve as a basis for choosing the appropriate algorithm for a new domain. Furthermore, the distilled challenges may assist in the design of new learning algorithms that overcome these problems and achieve higher performance in multi-agent applications.

Type
Articles
Information
The Knowledge Engineering Review , Volume 27 , Issue 1 , 22 February 2012 , pp. 1 - 31
Copyright
Copyright © Cambridge University Press 2012

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