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Interpreting a deep reinforcement learning model with conceptual embedding and performance analysis

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Abstract

The weak interpretability of the deep reinforcement learning (DRL) model becomes a serious impediment to the application of DRL agents in certain areas requiring high reliability. To interpret the behavior of a DRL agent, researchers use saliency maps to discover important parts of the agent’s observation that influence its decision. However, the representations of saliency maps still cannot explicitly present the cause and effect between an agent’s actions and its observations. In this paper, we analyze the inference procedure with respect to the DRL architecture and propose embedding interpretable intermediate representations for an agent’s policy, the intermediate representations that are compressed and abstracted for explanation. We utilize a conceptual embedding technique to regulate the latent representation space of the deep models that can produce interpretable causal factors aligned with human concepts. Furthermore, the information loss of intermediate representation is analyzed to define the model performance upper bound and to measure the model performance degeneration. Experiments validate the effectiveness of the proposed method and the relationship between the observation information and an agent’s performance upper bound.

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Acknowledgements

This work is supported in part by China Postdoctoral Science Foundation under Grant Number 2021M693976, the Hunan Provincial Natural Science Foundation under Grant Number 2020JJ5367, and the Key Project of Teaching Reform in Colleges and Universities of Hunan Province under Grant Number HNJG-2021-0251.

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Authors and Affiliations

  1. College of Liberal Arts and Sciences, National University of Defense Technology, Changsha, 410073, Hunan, China

    Yinglong Dai, Han Long & Xiaojun Duan

  2. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, 410081, Hunan, China

    Yinglong Dai

  3. School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, 510006, Guangdong, China

    Haibin Ouyang

  4. School of Physics and Electronics, Hunan Normal University, Changsha, 410081, Hunan, China

    Hong Zheng

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  1. Yinglong Dai
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  2. Haibin Ouyang
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  3. Hong Zheng
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  4. Han Long
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  5. Xiaojun Duan
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Correspondence to Yinglong Dai or Xiaojun Duan.

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Dai, Y., Ouyang, H., Zheng, H. et al. Interpreting a deep reinforcement learning model with conceptual embedding and performance analysis. Appl Intell 53, 6936–6952 (2023). https://doi.org/10.1007/s10489-022-03788-7

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