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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 sparsedoi:10.1145/1102351.1102472 dblp:conf/icml/WangLBS05 fatcat:pwpcdttqufbttklck3qpfxbsua