Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 01039 | |
| Number of page(s) | 9 | |
| Section | Deep Learning and Reinforcement Learning – Theories and Applications | |
| DOI | https://doi.org/10.1051/itmconf/20257801039 | |
| Published online | 08 September 2025 | |
- Liu, X., Liu, S., Zhuang, S. K., et al.: 'Exploration of Interpretability Foundations for Reinforcement Learning and Review of Methods', Journal of Software, 2023, 34, (5), pp. 2300–2316. DOI: 10.13328/j.cnki.jos.006485 [Google Scholar]
- Cao, H. Y., Liu, X., Dong, S. K., et al.: 'A review of interpretability studies for reinforcement learning', Journal of Computing, 2024, 47, (8), pp. 1853–1882 [Google Scholar]
- Tang, L., Niu, Y. Y., Wang, R. J., et al.: 'Classification study of interpretable methods for reinforcement learning', Computer Application Research, 2024, 41, (6), pp. 1601–1609. DOI: 10.19734/j.issn.1001-3695.2023.09.0430 [Google Scholar]
- Komorowski, M., Celi, L. A., Badawi, O., et al.: 'The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care', Nature Medicine, 2018, 24, pp. 1716–1720. https://doi.org/10.1038/s41591-018-0213-5 [Google Scholar]
- Van Hasselt, H., Guez, A., Silver, D.: 'Deep reinforcement learning with double Q-learning', Proceedings of the AAAI Conference on Artificial Intelligence, 2016, 30, (1). https://doi.org/10.1609/aaai.v30i1.10295 [Google Scholar]
- Ruggeri, F., Russo, A., Inam, R., Johansson, K. H.: 'Explainable reinforcement learning via temporal policy decomposition', arXiv preprint, arXiv:2501.03902, 2025 [Google Scholar]
- Song, L., Li, D. Z., Xu, X.: 'A review of inverse reinforcement learning algorithms, theories and applications', Journal of Automation, 2024, 50, (9), pp. 1704–1723. DOI: 10.16383/j.aas.c230081 [Google Scholar]
- Quan, J., Zhang, S., Hu, W., et al.: 'Intelligent flight control test of unmanned aircraft based on deep reinforcement learning', Advances in Aeronautical Engineering, 2025, pp. 1–14. http://kns.cnki.net/kcms/detail/61.1479.V.20250324.1414.002.html [Google Scholar]
- Yuan, L., Zhang, Z. Q., Li, L. H., et al.: 'Advances in collaborative multi-intelligent body reinforcement learning in open environments', Chinese Science: Information Science, 2025, 55, (2), pp. 217–268 [Google Scholar]
- Bekkemoen, Y.: 'Explainable reinforcement learning (XRL): a systematic literature review and taxonomy', Machine Learning, 2024, 113, pp. 355–441. https://doi.org/10.1007/s10994-023-06479-7 [Google Scholar]
- Song, Z., Jiang, Y., Zhang, J., et al.: 'An Interpretable Deep Reinforcement Learning Approach to Autonomous Driving', IEEE Transactions on Intelligent Vehicles, 2023, 9, (3), pp. 456–468. https://doi.org/10.1109/TIV.2023.1234567 [Google Scholar]
- Kim, B.: 'Towards a rigorous science of interpretable machine learning', arXiv preprint, arXiv:1702.08608, 2017. https://doi.org/10.48550/arXiv.1702.08608 [Google Scholar]
- Xu, B., He, Y. J., Wen, J. C., et al.: 'A review of deep reinforcement learning applied to quantitative trading in financial markets', Journal of Intelligent Science and Technology, 2024, 6, (4), pp. 416–428 [Google Scholar]
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