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A Review of Deep Reinforcement Learning Exploration Methods: Prospects and Challenges for Application to Robot Attitude Control Tasks

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Cognitive Systems and Information Processing (ICCSIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1787))

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Abstract

In recent years, deep reinforcement learning has been widely used in the real world, such as in robot control and autonomous driving. However, the sample-inefficient of deep reinforcement learning prevents its application in robot control. In addition, complex real-world scenarios require the high robustness of robot controllers, and the design of controllers needs to consider the influence of the external environment. These problems become severer in environments with high-dimensional state action spaces and sparse delay rewards. In this paper, we provide a systematic introduction and summary of the existing methods for exploration. Firstly, we introduce the primary exploration techniques and summarize the challenges faced by intelligent body exploration. Then, we classify the existing exploration methods in terms of whether they generate other bonuses or not and elaborate on the ideas of different methods. Finally, we discuss the challenges of applying deep reinforcement learning to robot control and the applicability of different exploration methods to attitude control tasks, ruling out exploration methods unsuitable for attitude control tasks for subsequent research.

CAS Project for Young Scientists in Basic Research, Grant No. YSBR-040.

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Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, China

    Chao Li, Fengge Wu & Junsuo Zhao

  2. Institute of Software Chinese Academy of Sciences (ISCAS), Beijing, China

    Chao Li, Fengge Wu & Junsuo Zhao

Authors
  1. Chao Li
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  2. Fengge Wu
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  3. Junsuo Zhao
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Corresponding author

Correspondence to Fengge Wu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. University of Manchester, Manchester, UK

    Angelo Cangelosi

  3. Universität Hamburg, Hamburg, Germany

    Jianwei Zhang

  4. Fuzhou University, Fuzhou, China

    Yuanlong Yu

  5. Tsinghua University, Beijing, China

    Huaping Liu

  6. Tsinghua University, Beijing, China

    Bin Fang

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Li, C., Wu, F., Zhao, J. (2023). A Review of Deep Reinforcement Learning Exploration Methods: Prospects and Challenges for Application to Robot Attitude Control Tasks. In: Sun, F., Cangelosi, A., Zhang, J., Yu, Y., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2022. Communications in Computer and Information Science, vol 1787. Springer, Singapore. https://doi.org/10.1007/978-981-99-0617-8_18

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  • DOI: https://doi.org/10.1007/978-981-99-0617-8_18

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