Code and Data Repository for An Efficient Node Selection Policy for Monte Carlo Tree Search with Neural Networks

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper An Efficient Node Selection Policy for Monte Carlo Tree Search with Neural Networks by Xiaotian Liu, Yijie Peng, Gongbo Zhang, and Ruihan Zhou.

Authors

Yijie Peng (ORCID: https://orcid.org/0000-0003-2584-8131)
Gongbo Zhang (ORCID: https://orcid.org/0000-0002-7517-7666)
Ruihan Zhou (ORCID: https://orcid.org/0009-0001-1498-7702)
Xiaotian Liu (ORCID: https://orcid.org/0009-0009-7018-8030)

Publication Details

Journal: INFORMS journal on computing
Published: 2024-09-18
DOI: https://doi.org/10.1287/ijoc.2023.0307.cd
Citations: 3
Primary Topic: Machine Learning and Data Classification
Type: article
Field-Weighted Citation Impact: 1.04

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article

Code and Data Repository for An Efficient Node Selection Policy for Monte Carlo Tree Search with Neural Networks

Yijie Peng, Gongbo Zhang, Ruihan Zhou, Xiaotian Liu

Sep 18, 2024

3 citations

INFORMS journal on computing

Machine Learning and Data Classification

1.04

article

Code and Data Repository for An Efficient Node Selection Policy for Monte Carlo Tree Search with Neural Networks

Yijie Peng, Gongbo Zhang, Ruihan Zhou, Xiaotian Liu

September 18, 2024article en

3 citations

Abstract

INFORMS journal on computing

Openalex Percentile: Top 19%

Machine Learning and Data Classification

1.04

DOI Source OpenAlex

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Code and Data Repository for An Efficient Node Selection Policy for Monte Carlo Tree Search with Neural Networks — Yijie Peng, Gongbo Zhang, et al. · INFORMS journal on computing (2024) | TGRS Research Map | TGRS