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ACMViz: a visual analytics approach to understand DRL-based autonomous control model

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Abstract

Deep reinforcement learning (DRL) has been widely used in autonomous control due to its superior performance. DRL-based autonomous control model (ACM) aims to train an agent to achieve self-control and learn optimal policy through pre-defined rewards. Despite the super-human performance, ACM is regarded as a black box, and the interpretation of its internal working mechanism remains a challenge to domain experts. In addition, adjusting the reward settings of ACM is also challenging due to the uncertain relationship between rewards setting and strategies. In this paper, we propose ACMViz, a visual analytics system to explore control strategies at different stages and reveal the relationship between rewards and action patterns. Focusing on controlling a lunar lander, ACMViz investigates different landing trajectories and action sequences to interpret the model and control the training. From our visual analytics of the action patterns, we diagnose and improve reward settings for different control targets. Through our case studies with deep learning experts, we validate the effectiveness of ACMViz.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2020YFB0204802).

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Authors and Affiliations

  1. Computer Network Information Center, Chinese Academy of Sciences, Beijing, China

    Shiyu Cheng, Guihua Shan, Beifang Niu & Yang Wang

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

    Shiyu Cheng, Guihua Shan, Beifang Niu & Yang Wang

  3. The Ohio State University, Columbus, OH, USA

    Xiaochen Li

  4. Sichuan University, Chengdu, China

    MaoKang Luo

Authors
  1. Shiyu Cheng
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  2. Xiaochen Li
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  3. Guihua Shan
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  4. Beifang Niu
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  5. Yang Wang
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  6. MaoKang Luo
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Correspondence to Guihua Shan or Beifang Niu.

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Cheng, S., Li, X., Shan, G. et al. ACMViz: a visual analytics approach to understand DRL-based autonomous control model. J Vis 25, 427–442 (2022). https://doi.org/10.1007/s12650-021-00793-9

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  • DOI: https://doi.org/10.1007/s12650-021-00793-9

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