Open Access
| Issue |
ITM Web Conf.
Volume 80, 2025
2025 2nd International Conference on Advanced Computer Applications and Artificial Intelligence (ACAAI 2025)
|
|
|---|---|---|
| Article Number | 01043 | |
| Number of page(s) | 6 | |
| Section | Machine Learning & Deep Learning Algorithms | |
| DOI | https://doi.org/10.1051/itmconf/20258001043 | |
| Published online | 16 December 2025 | |
- A. Coronato, M. Naeem, G. De Pietro, G. Paragliola, Reinforcement learning for intelligent healthcare applications: A survey. Artif. Intell. Med. 109, 101964 (2020) [Google Scholar]
- P. Wang, C.-Y. Chan, A. de La Fortelle, A reinforcement learning based approach for automated lane change maneuvers. Proc. IEEE Intell. Veh. Symp. (IV), 1379–1384 (2018) [Google Scholar]
- X. Wang, S. Wang, X. Liang, D. Zhao, J. Huang, X. Xu, B. Dai, Q. Miao, Deep reinforcement learning: A survey. IEEE Trans. Neural Netw. Learn. Syst. 35(4), 5064–5078 (2024) [Google Scholar]
- R. F. Prudencio, M. R. O. A. Maximo, E. L. Colombini, A survey on offline reinforcement learning: Taxonomy, review, and open problems. IEEE Trans. Neural Netw. Learn. Syst. 35(8), 10237–10257 (2024) [Google Scholar]
- S. Fujimoto, D. Meger, D. Precup, Off-policy deep reinforcement learning without exploration. Proc. 36th Int. Conf. Mach. Learn. (ICML), 2052–2062 (2019) [Google Scholar]
- A. Kumar, A. Zhou, G. Tucker, S. Levine, Conservative Q-learning for offline reinforcement learning. Adv. Neural Inf. Process. Syst. 33, 1179–1191 (2020) [Google Scholar]
- J. Fu, A. Kumar, O. Nachum, G. Tucker, S. Levine, D4RL: Datasets for deep data- driven reinforcement learning. arXiv preprint arXiv:2004.07219 (2020) [Google Scholar]
- B. Jang, M. Kim, G. Harerimana, J. W. Kim, Q-learning algorithms: A comprehensive classification and applications. IEEE Access 7, 133653–133667 (2019) [Google Scholar]
- T. Eimer, M. Lindauer, R. Raileanu, Hyperparameters in reinforcement learning and how to tune them. Proc. 40th Int. Conf. Mach. Learn. (ICML) 202, 9104–9149 (2023) [Google Scholar]
- M. T. Young, J. D. Hinkle, R. Kannan, A. Ramanathan, Distributed Bayesian optimization of deep reinforcement learning algorithms. J. Parallel Distrib. Comput. 139, 43–52 (2020) [Google Scholar]
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