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An Optimal Computing Budget Allocation Tree Policy for Monte Carlo Tree Search
[article]
2020
arXiv
pre-print
We analyze a tree search problem with an underlying Markov decision process, in which the goal is to identify the best action at the root that achieves the highest cumulative reward. We present a new tree policy that optimally allocates a limited computing budget to maximize a lower bound on the probability of correctly selecting the best action at each node. Compared to widely used Upper Confidence Bound (UCB) tree policies, the new tree policy presents a more balanced approach to manage the
arXiv:2009.12407v1
fatcat:tb6xevspffhdzecvlfskulavqi