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Abstract: This paper introduces Correlated-Q (CE-Q) learning, a multiagent Q-learning algorithm based on the correlated equilibrium (CE) solution concept. CE-Q generalizes both Nash- Q and Friend-and-Foe-Q: in general-sum games, the set of correlated equilibria contains the set of Nash equilibria; in constantsum games, the set of correlated equilibria contains the set of minimax equilibria. This paper describes experiments with four variants of CE-Q, demonstrating empirical convergence to... (Update)
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BibTeX entry: (Update)
@misc{ amy-correlatedq,
author = "Amy Greenwald Amy",
title = "Correlated-Q Learning",
url = "citeseer.ist.psu.edu/753783.html" }
Citations (may not include all citations):199 Markov games as a framework for multiagent reinforcement lea.. - Littman - 1994
182 The Theory of Games and Economic Behavior (context) - von Neumann, Morgenstern - 1944
125 Non-cooperative games (context) - Nash - 1951
99 Multiagent reinforcement learning: Theoretical framework and.. - Hu, Wellman - 1998
41 A generalized reinforcement learning model: Convergence and .. - Littman, Szepesv - 1996
38 Regret in the on-line decision problem - Foster, Vohra - 1997
22 Subjectivity and correlation in randomized strategies (context) - Aumann - 1974
11 line prediction (context) - Freund, Schapire et al. - 1996
11 Stochastic evolutionary game dynamics - Foster, Young - 1990
5 On no-regret learning (context) - Greenwald, Jafari et al. - 2001
4 Friend or foe Q-learning in general-sum Markov games (context) - Littman - 2001
3 On no-regret learning and game-theoretic equilibria (context) - Greenwald, Gondek et al. - 2001
3 Experimental results of multiagent reinforcement learning (context) - Hu, Wellman - 2000
3 Equilibrium policies in Markov games (context) - Greenwald - 2002
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