Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 01006 | |
| Number of page(s) | 7 | |
| Section | Deep Learning and Reinforcement Learning – Theories and Applications | |
| DOI | https://doi.org/10.1051/itmconf/20257801006 | |
| Published online | 08 September 2025 | |
- Eimer, T., Lindauer, M., Raileanu, R.: 'Hyperparameters in reinforcement learning and how to tune them'. Proc. Int. Conf. Mach. Learn. (ICML), July 14, pp. 9104 [Google Scholar]
- Mnih, V., Kavukcuoglu, K., Silver, D., et al.: 'Human-level control through deep reinforcement learning', Nature, 2015, 518, (7540), pp. 529–533 [NASA ADS] [CrossRef] [Google Scholar]
- Even-Dar, E., Mansour, Y., Bartlett, P.: 'Learning rates for Q-learning', J. Mach. Learn. Res., 2003, 5, (1) [Google Scholar]
- Yu, T., Zhu, H.: 'Hyper-parameter optimization: A review of algorithms and applications', arXiv preprint, 2020, arXiv:2003.05689 [Google Scholar]
- Li, L., Jamieson, K., Rostamizadeh, A., et al.: 'Massively parallel hyperparameter tuning', arXiv preprint, 2018, arXiv:1810.05934 [Google Scholar]
- Hutter, F., Hoos, H.H., Leyton-Brown, K.: 'Sequential model-based optimization for general algorithm configuration'. Proc. 5th Int. Conf. Learn. Intell. Optim. (LION), Rome, Italy, January 14, pp. 507 [Google Scholar]
- Xu, C., Qin, T., Wang, G., Liu, T.Y.: 'Reinforcement learning for learning rate control', arXiv preprint, 2017, arXiv:1705.11159 [Google Scholar]
- Kiran, M., Ozyildirim, M.: 'Hyperparameter tuning for deep reinforcement learning applications', arXiv preprint, 2022, arXiv:2201.11182 [Google Scholar]
- Kumar, S.: 'Balancing a CartPole system with reinforcement learning–A tutorial', arXiv preprint, 2020, arXiv:2006.04938 [Google Scholar]
- Akiba, T., Sano, S., Yanase, T., et al.: 'Optuna: A next-generation hyperparameter optimization framework'. Proc. 25th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (KDD), July 14, pp. 2623 [Google Scholar]
- Glorot, X., Bordes, A., Bengio, Y.: 'Deep sparse rectifier neural networks'. Proc. 14th Int. Conf. Artif. Intell. Stat. (AISTATS), June 14, pp. 315 [Google Scholar]
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