Open Access
| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 03008 | |
| Number of page(s) | 8 | |
| Section | Large Language Models, Generative AI, and Multimodal Learning | |
| DOI | https://doi.org/10.1051/itmconf/20268403008 | |
| Published online | 06 April 2026 | |
- Q. Wen, W. Chen, L. Sun, Z. Zhang, L. Wang, R. Jin, T. Tan, Onenet: Enhancing time series forecasting models under concept drift by online ensembling. Adv. Neural Inf. Process. Syst. 36, 69949–69980 (2023) [Google Scholar]
- B. Kim, M.H. Oh, Model-based offline reinforcement learning with count-based conservatism, in Proceedings of the International Conference on Machine Learning, 16728–16746 (2023) [Google Scholar]
- T. Xie, D.J. Foster, Y. Bai, N. Jiang, S.M. Kakade, The role of coverage in online reinforcement learning. arXiv:2210.04157 (2022) [Google Scholar]
- C. Lu, P.J. Ball, T.G. Rudner, J. Parker-Holder, M.A. Osborne, Y.W. Teh, Challenges and opportunities in offline reinforcement learning from visual observations. arXiv:2206.04779 (2022) [Google Scholar]
- T. Haarnoja, A. Zhou, P. Abbeel, S. Levine, Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, in Proceedings of the International Conference on Machine Learning, 1861–1870 (2018) [Google Scholar]
- J. Schulman, F. Wolski, P. Dhariwal, A. Radford, O. Klimov, Proximal policy optimization algorithms. arXiv:1707.06347 (2017) [Google Scholar]
- L. Wang, X. Hu, Y. Wang, S. Xu, S. Ma, K. Yang, W. Wang, Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning. Comput. Netw. 190, 107969 (2021) [Google Scholar]
- C. Yu, A. Velu, E. Vinitsky, J. Gao, Y. Wang, A. Bayen, Y. Wu, The surprising effectiveness of PPO in cooperative multi-agent games. Adv. Neural Inf. Process. Syst. 35, 24611–24624 (2022) [Google Scholar]
- R. Reiter, A. Ghezzi, K. Baumgärtner, J. Hoffmann, R.D. McAllister, M. Diehl, AC4MPC: Actor-critic reinforcement learning for nonlinear model predictive control. arXiv:2406.03995 (2024) [Google Scholar]
- Z. Jia, A. Rakhlin, A. Sekhari, C.Y. Wei, Offline reinforcement learning: Role of state aggregation and trajectory data, in Proceedings of the Conference on Learning Theory, 2644–2719 (2024) [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. Lyu, X. Ma, X. Li, Z. Lu, Mildly conservative Q-learning for offline reinforcement learning. Adv. Neural Inf. Process. Syst. 35, 1711–1724 (2022) [Google Scholar]
- J. Luo, P. Dong, J. Wu, A. Kumar, X. Geng, S. Levine, Action-quantized offline reinforcement learning for robotic skill learning, in Proceedings of the Conference on Robot Learning, 1348–1361 (2023) [Google Scholar]
- I. Kostrikov, A. Nair, S. Levine, Offline reinforcement learning with implicit Q- learning. arXiv:2110.06169 (2021) [Google Scholar]
- T. Matsushima, H. Furuta, Y. Matsuo, O. Nachum, S. Gu, Deployment-efficient reinforcement learning via model-based offline optimization. arXiv:2006.03647 (2020) [Google Scholar]
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