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Bayesian sparse sampling for on-line reward optimization
2005
Proceedings of the 22nd international conference on Machine learning - ICML '05
We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration versus exploitation tradeoff. Our approach combines sparse sampling with Bayesian exploration to achieve improved decision making while controlling computational cost. The idea is to grow a sparse lookahead tree, intelligently, by exploiting information in a Bayesian posterior-rather than enumerate action branches (standard sparse
doi:10.1145/1102351.1102472
dblp:conf/icml/WangLBS05
fatcat:pwpcdttqufbttklck3qpfxbsua