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Improving deep reinforcement learning by safety guarding model via hazardous experience planning

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61303108), Natural Science Foundation of Jiangsu Province (BK20211102), Suzhou Key Industries Technological Innovation-Prospective Applied Research Project (SYG 201804); A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Pai Peng, Fei Zhu, Xinghong Ling, Peiyao Zhao & Quan Liu

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  1. Pai Peng
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  2. Fei Zhu
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  3. Xinghong Ling
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  4. Peiyao Zhao
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  5. Quan Liu
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Corresponding author

Correspondence to Fei Zhu.

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The supporting information is available online at journal. hep. com. cn and link. springer. com

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Peng, P., Zhu, F., Ling, X. et al. Improving deep reinforcement learning by safety guarding model via hazardous experience planning. Front. Comput. Sci. 16, 164320 (2022). https://doi.org/10.1007/s11704-021-0250-y

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