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An Efficient Dynamic Sampling Policy For Monte Carlo Tree Search

2022-04-26 02:39:18
Gongbo Zhang, Yijie Peng, Yilong Xu
arXiv_AI
       
Abstract

We consider the popular tree-based search strategy within the framework of reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of finite-horizon Markov decision process. We propose a dynamic sampling tree policy that efficiently allocates limited computational budget to maximize the probability of correct selection of the best action at the root node of the tree. Experimental results on Tic-Tac-Toe and Gomoku show that the proposed tree policy is more efficient than other competing methods.

Abstract (translated)

URL

https://arxiv.org/abs/2204.12043

PDF

https://arxiv.org/pdf/2204.12043.pdf

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