By Topic

Adaptive Adversarial Multi-Armed Bandit Approach to Two-Person Zero-Sum Markov Games

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Hyeong Soo Chang ; Dept. of Comput. Sci. & Eng., Sogang Univ., Seoul, South Korea ; Jiaqiao Hu ; Fu, M.C. ; Marcus, S.I.

This technical note presents a recursive sampling-based algorithm for finite horizon two-person zero-sum Markov games (MGs) based on the Exp3 algorithm developed by Auer et al. for adaptive adversarial multi-armed bandit problems. We provide a finite-iteration bound to the equilibrium value of the induced ??sample average approximation game?? of a given MG and prove asymptotic convergence to the equilibrium value of the given MG. The time and space complexities of the algorithm are independent of the state space of the game.

Published in:

Automatic Control, IEEE Transactions on  (Volume:55 ,  Issue: 2 )