Bayesian sparse sampling for on-line reward optimization

Tao Wang, Daniel Lizotte, Michael Bowling, Dale Schuurmans
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
more » ... mpling) or compensate myopically (value of perfect information). The outcome is a flexible, practical technique for improving action selection in simple reinforcement learning scenarios.
doi:10.1145/1102351.1102472 dblp:conf/icml/WangLBS05 fatcat:pwpcdttqufbttklck3qpfxbsua