Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1022-1028

Method for inhomogeneous multi-task reinforcement learning based on morphological information encoding by graph embedding

He Xiao1,2,3
Wang Wenxue1,2
1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2. Institutes for Robotics & Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0373

Abstract

Traditional reinforcement learning methods have problems of low efficiency, poor generalization performance, and untransferable policy models. In response to this issue, this paper proposed an inhomogeneous multitask reinforcement learning method, which improved efficiency and generalization performance by learning multiple reinforcement tasks. It constructed the morphology of agent into a graph, and the graph neural network could handle graphs with any connection pattern and size graph, which was really suitable to solve inhomogeneous tasks with different dimensions of state and action space. This breaks through the limitations that model couldn't be transferred and fully utilizes the advantages of graph neural network's natural use of graph structure to induce bias. The model had achieved efficient training and improved generalization performance, and could be quickly migrated to new tasks. The results of multi task learning experiments show that compared with previous methods, this method exhibits better performance in both multi task learning and transfer learning experiments, and exhibits more accurate knowledge transfer in transfer learning experiments. By introducing bias in the structure of the agent graph, this method has achieved higher efficiency and better migration generalization performance.

Foundation Support

国家自然科学基金资助项目(U1908215)
辽宁省“兴辽英才计划”资助项目(XLYC2002014)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0373
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: Algorithm Research & Explore
Pages: 1022-1028
Serial Number: 1001-3695(2024)04-009-1022-07

Publish History

[2023-11-02] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

贺晓, 王文学. 基于图嵌入编码形态信息的非均匀多任务强化学习方法 [J]. 计算机应用研究, 2024, 41 (4): 1022-1028. (He Xiao, Wang Wenxue. Method for inhomogeneous multi-task reinforcement learning based on morphological information encoding by graph embedding [J]. Application Research of Computers, 2024, 41 (4): 1022-1028. )

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.

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