用神经网络进行蒙特卡洛树搜索的高效节点选择策略

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Informs Journal on Computing Pub Date : 2024-09-18 DOI:10.1287/ijoc.2023.0307
Xiaotian Liu, Yijie Peng, Gongbo Zhang, Ruihan Zhou
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INFORMS 计算期刊》,印刷版。
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An Efficient Node Selection Policy for Monte Carlo Tree Search with Neural Networks
INFORMS Journal on Computing, Ahead of Print.
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来源期刊
Informs Journal on Computing
Informs Journal on Computing 工程技术-计算机:跨学科应用
CiteScore
4.20
自引率
14.30%
发文量
162
审稿时长
7.5 months
期刊介绍: The INFORMS Journal on Computing (JOC) is a quarterly that publishes papers in the intersection of operations research (OR) and computer science (CS). Most papers contain original research, but we also welcome special papers in a variety of forms, including Feature Articles on timely topics, Expository Reviews making a comprehensive survey and evaluation of a subject area, and State-of-the-Art Reviews that collect and integrate recent streams of research.
期刊最新文献
An Efficient Node Selection Policy for Monte Carlo Tree Search with Neural Networks Combining Precision Boosting with LP Iterative Refinement for Exact Linear Optimization Pairwise Stability in Weighted Network Formation Games: Selection and Computation Forecasting Urban Traffic States with Sparse Data Using Hankel Temporal Matrix Factorization Multiobjective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural Networks
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