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Reinforcement Learning in Few-Shot Scenarios: A Survey

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Abstract

Reinforcement learning has a demand for massive data in complex problems, which makes it infeasible to be applied to real cases where sampling is difficult. The key to coping with these few-shot problems is knowledge generalization, and related algorithms are often called few-shot reinforcement learning (FS-RL). However, there lacks a formal definition and comprehensive analyses of few-shot scenarios and FS-RL algorithms. Therefore, after giving a uniform definition, we categorize few-shot scenarios into two types. The first type pursues more professional performance, while the other one pursues more general performance. In the process of knowledge transfer, few-shot scenarios usually have an obvious tendency to some type of knowledge. Based on this, we divide FS-RL algorithms into two types: the direct transfer case and the indirect transfer case. Thereafter, existing algorithms are discussed under this classification. Finally, we discuss future directions of FS-RL from the aspect of both theory and application.

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Acknowledgements

This work was financially supported by Primary Research and Development Plan of China (No.2020YF-C2006602), National Natural Science Foundation of China (No.62072324, No.61876217, No.61876121, No.61772357), University Natural Science Foundation of Jiangsu Province (No.21KJA520005), Primary Research and Development Plan of Jiangsu Province (No.BE2020026), Natural Science Foundation of Jiangsu Province (No.BK20190942), Postgraduate Research & Practice Innovation Program of Jiangsu Province (No.KYCX 21_3020).

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  1. School of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China

    Zhechao Wang, Qiming Fu, Yunzhe Wang, You Lu & Hongjie Wu

  2. Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, 215009, China

    Zhechao Wang, Qiming Fu, Jianping Chen, Yunzhe Wang, You Lu & Hongjie Wu

  3. School of Architecture and Urban Planning, Suzhou University of Science and Technology, Suzhou, 215009, China

    Jianping Chen

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Wang, Z., Fu, Q., Chen, J. et al. Reinforcement Learning in Few-Shot Scenarios: A Survey. J Grid Computing 21, 30 (2023). https://doi.org/10.1007/s10723-023-09663-0

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