Correlated-Q learning (2003)
| Venue: | In AAAI Spring Symposium |
| Citations: | 34 - 0 self |
BibTeX
@INPROCEEDINGS{Greenwald03correlated-qlearning,
author = {Amy Greenwald and Keith Hall},
title = {Correlated-Q learning},
booktitle = {In AAAI Spring Symposium},
year = {2003},
pages = {242--249},
publisher = {AAAI Press}
}
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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 equilibrium policies on a testbed of general-sum Markov games. 1.
