In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 4

DPC-DQRL: offline to online double Q value reinforcement learning with dynamic behavior cloning constraints

Yan Leiming1,2
Liu Jian1,2
Zhu Yongxin1,2
1. School of Computer Science & School of Cyber Science & Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
2. Engineering Research Center of Digital Forensics Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China
DOI: 10.19734/j.issn.1001-3695.2024.09.0338

Abstract

Offline to online reinforcement learning focuses on improving the performance of pre-trained models through minimal online fine-tuning. Existing methods primarily adopt unconstrained or constrained fine-tuning. The unconstrained approach often results in severe policy collapse due to significant distribution shifts, while the constrained approach slows performance improvement because of strict offline constraints, reducing training efficiency. To address these limitations, this study identifies inaccurate Q value estimation as a primary factor affecting performance through a comparative visualization of the fine-tuning processes of both approaches. To mitigate this issue, this paper proposes a Dynamic Policy-Constrained Double Q Value Reinforcement Learning (DPC-DQRL) algorithm. The method incorporates a dynamic behavior cloning constraint based on a memory-forgetting mechanism, which dynamically adjusts constraint strength during fine-tuning. Furthermore, an offline-online double Q value network is constructed by integrating an offline action-value network into Q value estimation, enhancing Q value accuracy in the fine-tuning phase. Using the Gym simulation platform with the MuJoCo physics engine, DPC-DQRL was applied to fine-tune three classic tasks: Halfcheetah, Hopper, and Walker2d. The performance after fine-tuning improved by 47%, 63%, and 20%, respectively, compared to the original pre-trained model. The average normalized scores across all tasks showed a 10% improvement over the optimal baseline algorithm. The experimental results demonstrate that DPC-DQRL enhances model performance while maintaining stability, showcasing significant advantages over other algorithms.

Foundation Support

国家自然科学基金资助项目(62172292,42375147)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.09.0338
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 4

Publish History

[2024-12-25] Accepted Paper

Cite This Article

闫雷鸣, 刘健, 朱永昕. DPC-DQRL:动态行为克隆约束的离线-在线双Q值强化学习 [J]. 计算机应用研究, 2025, 42 (4). (2024-12-31). https://doi.org/10.19734/j.issn.1001-3695.2024.09.0338. (Yan Leiming, Liu Jian, Zhu Yongxin. DPC-DQRL: offline to online double Q value reinforcement learning with dynamic behavior cloning constraints [J]. Application Research of Computers, 2025, 42 (4). (2024-12-31). https://doi.org/10.19734/j.issn.1001-3695.2024.09.0338. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)