{"title":"LsAc ∗-MJ:麻将游戏的低资源消耗强化学习模型","authors":"Xiali Li, Zhaoqi Wang, Bo Liu, Junxue Dai","doi":"10.1155/2024/4558614","DOIUrl":null,"url":null,"abstract":"<div>\n <p>This article proposes a novel Mahjong game model, LsAc <sup>∗</sup>-MJ, designed to address challenges posed by data scarcity, difficulty in leveraging contextual information, and the computational resource-intensive nature of self-play zero-shot learning. The model is applied to Japanese Mahjong for experiments. LsAc <sup>∗</sup>-MJ employs long short-term memory (LSTM) neural networks, utilizing hidden nodes to store and propagate contextual historical information, thereby enhancing decision accuracy. Additionally, the paper introduces an optimized Advantage Actor-Critic (A2C) algorithm incorporating an experience replay mechanism to enhance the model’s decision-making capabilities and mitigate convergence difficulties arising from strong data correlations. Furthermore, the paper presents a two-stage training approach for self-play deep reinforcement learning models guided by expert knowledge, thereby improving training efficiency. Extensive ablation experiments and performance comparisons demonstrate that, in contrast to other typical deep reinforcement learning models on the RLcard platform, the LsAc <sup>∗</sup>-MJ model consumes lower computational and time resources, has higher training efficiency, faster average decision time, higher win-rate, and stronger decision-making ability.</p>\n </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4558614","citationCount":"0","resultStr":"{\"title\":\"LsAc ∗-MJ: A Low-Resource Consumption Reinforcement Learning Model for Mahjong Game\",\"authors\":\"Xiali Li, Zhaoqi Wang, Bo Liu, Junxue Dai\",\"doi\":\"10.1155/2024/4558614\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p>This article proposes a novel Mahjong game model, LsAc <sup>∗</sup>-MJ, designed to address challenges posed by data scarcity, difficulty in leveraging contextual information, and the computational resource-intensive nature of self-play zero-shot learning. The model is applied to Japanese Mahjong for experiments. LsAc <sup>∗</sup>-MJ employs long short-term memory (LSTM) neural networks, utilizing hidden nodes to store and propagate contextual historical information, thereby enhancing decision accuracy. Additionally, the paper introduces an optimized Advantage Actor-Critic (A2C) algorithm incorporating an experience replay mechanism to enhance the model’s decision-making capabilities and mitigate convergence difficulties arising from strong data correlations. Furthermore, the paper presents a two-stage training approach for self-play deep reinforcement learning models guided by expert knowledge, thereby improving training efficiency. Extensive ablation experiments and performance comparisons demonstrate that, in contrast to other typical deep reinforcement learning models on the RLcard platform, the LsAc <sup>∗</sup>-MJ model consumes lower computational and time resources, has higher training efficiency, faster average decision time, higher win-rate, and stronger decision-making ability.</p>\\n </div>\",\"PeriodicalId\":14089,\"journal\":{\"name\":\"International Journal of Intelligent Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4558614\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Intelligent Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1155/2024/4558614\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2024/4558614","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
LsAc ∗-MJ: A Low-Resource Consumption Reinforcement Learning Model for Mahjong Game
This article proposes a novel Mahjong game model, LsAc ∗-MJ, designed to address challenges posed by data scarcity, difficulty in leveraging contextual information, and the computational resource-intensive nature of self-play zero-shot learning. The model is applied to Japanese Mahjong for experiments. LsAc ∗-MJ employs long short-term memory (LSTM) neural networks, utilizing hidden nodes to store and propagate contextual historical information, thereby enhancing decision accuracy. Additionally, the paper introduces an optimized Advantage Actor-Critic (A2C) algorithm incorporating an experience replay mechanism to enhance the model’s decision-making capabilities and mitigate convergence difficulties arising from strong data correlations. Furthermore, the paper presents a two-stage training approach for self-play deep reinforcement learning models guided by expert knowledge, thereby improving training efficiency. Extensive ablation experiments and performance comparisons demonstrate that, in contrast to other typical deep reinforcement learning models on the RLcard platform, the LsAc ∗-MJ model consumes lower computational and time resources, has higher training efficiency, faster average decision time, higher win-rate, and stronger decision-making ability.
期刊介绍:
The International Journal of Intelligent Systems serves as a forum for individuals interested in tapping into the vast theories based on intelligent systems construction. With its peer-reviewed format, the journal explores several fascinating editorials written by today''s experts in the field. Because new developments are being introduced each day, there''s much to be learned — examination, analysis creation, information retrieval, man–computer interactions, and more. The International Journal of Intelligent Systems uses charts and illustrations to demonstrate these ground-breaking issues, and encourages readers to share their thoughts and experiences.