Hybrid Online and Offline Reinforcement Learning for Tibetan Jiu Chess

Author:

Li Xiali1ORCID,Lv Zhengyu1ORCID,Wu Licheng1,Zhao Yue1,Xu Xiaona1ORCID

Affiliation:

1. School of Information and Engineering, Minzu University of China, Beijing 100081, China

Abstract

In this study, hybrid state-action-reward-state-action (SARSAλ) and Q-learning algorithms are applied to different stages of an upper confidence bound applied to tree search for Tibetan Jiu chess. Q-learning is also used to update all the nodes on the search path when each game ends. A learning strategy that uses SARSAλ and Q-learning algorithms combining domain knowledge for a feedback function for layout and battle stages is proposed. An improved deep neural network based on ResNet18 is used for self-play training. Experimental results show that hybrid online and offline reinforcement learning with a deep neural network can improve the game program’s learning efficiency and understanding ability for Tibetan Jiu chess.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

Cited by 4 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. Tibetan Jiu Chess Intelligent Game Platform;Communications in Computer and Information Science;2024

3. A phased game algorithm combining deep reinforcement learning and UCT for Tibetan Jiu chess;2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC);2023-06

4. The Survey of Self-play Method in Computer Games;Cognitive Computation and Systems;2023

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