A phased game algorithm combining deep reinforcement learning and UCT for Tibetan Jiu chess-Reference-Cited by-同舟云学术

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A phased game algorithm combining deep reinforcement learning and UCT for Tibetan Jiu chess

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)
language:
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Author:

Li Xiali¹,Chen Yandong¹,Zhang Yanyin¹,Liu Bo¹,Wu Licheng¹

Affiliation:

1. Minzu University of China,School of Information and Engineering,Beijing,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10196792/10196808/10196898.pdf?arnumber=10196898

Reference17 articles.

1. Modifications of UCT and sequence-like simulations for Monte-Carlo Go

2. Combining online and offline knowledge in UCT

3. Research on Incomplete Information Game Opponent Model Based on Deep Learning;wang;Dissertation,2022

4. Research on Group Game Technologies Based on Attention Mechanism and Entropy Regularization;kou;Dissertation,2021

5. Applying Linear Invariance to Study Properties of Two-dimensional Normal Distribution;xu;Studies in College Mathematics,2021

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Nested Three-Stage Game Algorithm Based on Chess Shape Evaluation for Tibetan Jiu Chess;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

2. A Survey of Deep Reinforcement Learning in Game Playing;2024 MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon);2024-04-25

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