Survey on meta reinforcement learning

Zhao Chunyu
Lai Jun
College of Command & Control Engineering, Army Engineering University of PLA, Nanjing 210007, China

Abstract

Although reinforcement learning shows good performance in game playing, system control and other fields, how to use a small number of samples to learn new tasks quickly is an urgent problem to be solved in reinforcement learning. At present, applying meta learning to reinforcement learning has been one of the most effective solutions, and the generated meta reinforcement learning has increasingly become a research hotspot in the field of reinforcement learning. To help researchers understand the field of meta reinforcement learning quickly, this paper sorted out the algorithms according to the literatures of meta reinforcement learning in recent years, summarized them into CNN-based meta-RL, context-based meta-RL, gradient-based meta-RL, hierarchical-based meta-RL and offline meta-RL and compared five types of algorithms. In addition, it briefly described the basic theories and challenges of meta reinforcement learning. Finally, this paper also discussed the future development of meta reinforcement learning based on the current research status.

Foundation Support

国家自然科学基金资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.06.0295
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 1
Section: Survey
Pages: 1-10
Serial Number: 1001-3695(2023)01-001-0001-10

Publish History

[2022-08-22] Accepted Paper
[2023-01-05] Printed Article

Cite This Article

赵春宇, 赖俊. 元强化学习综述 [J]. 计算机应用研究, 2023, 40 (1): 1-10. (Zhao Chunyu, Lai Jun. Survey on meta reinforcement learning [J]. Application Research of Computers, 2023, 40 (1): 1-10. )

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.

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