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dc.contributor.authorSun, Yuxiang
dc.contributor.authorYuan, Bo
dc.contributor.authorZhang, Tao
dc.contributor.authorTang, Bojian
dc.contributor.authorZheng, Wanwen
dc.contributor.authorZhou, Xianzhong
dc.date.accessioned2020-11-06T11:34:07Z
dc.date.available2020-11-06T11:34:07Z
dc.date.issued2020-10-13
dc.identifier.citationSun, Y., Yuan, B., Zhang, T., Tang, B., Zheng, W. and Zhou, X., (2020). 'Research and implementation of intelligent decision based on a priori knowledge and DQN algorithms in wargame environment'. Electronics, 9(10), pp. 1-21.en_US
dc.identifier.doi10.3390/electronics9101668
dc.identifier.urihttp://hdl.handle.net/10545/625346
dc.description.abstractThe reinforcement learning problem of complex action control in a multi-player wargame has been a hot research topic in recent years. In this paper, a game system based on turn-based confrontation is designed and implemented with state-of-the-art deep reinforcement learning models. Specifically, we first design a Q-learning algorithm to achieve intelligent decision-making, which is based on the DQN (Deep Q Network) to model complex game behaviors. Then, an a priori knowledge-based algorithm PK-DQN (Prior Knowledge-Deep Q Network) is introduced to improve the DQN algorithm, which accelerates the convergence speed and stability of the algorithm. The experiments demonstrate the correctness of the PK-DQN algorithm, it is validated, and its performance surpasses the conventional DQN algorithm. Furthermore, the PK-DQN algorithm shows effectiveness in defeating the high level of rule-based opponents, which provides promising results for the exploration of the field of smart chess and intelligent game deductionen_US
dc.description.sponsorshipInnovation and Creativity Research Program for Doctoral Students of Nanjing University (grant number CXCY19-19)en_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.urlhttps://www.mdpi.com/2079-9292/9/10/1668en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectDQN algorithm; policy modeling; prior knowledge; intelligent decisionen_US
dc.titleResearch and implementation of intelligent decision based on a priori knowledge and DQN algorithms in wargame environmenten_US
dc.typeArticleen_US
dc.identifier.eissn2079-9292
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentNanjing University, Chinaen_US
dc.identifier.journalElectronicsen_US
dc.identifier.piielectronics9101668
dc.source.journaltitleElectronics
dc.source.volume9
dc.source.issue10
dc.source.beginpage1668
dcterms.dateAccepted2020-10-06
dc.author.detailSTF1867en_US


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Attribution-NonCommercial-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International