Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 01021 | |
| Number of page(s) | 10 | |
| Section | Deep Learning and Reinforcement Learning – Theories and Applications | |
| DOI | https://doi.org/10.1051/itmconf/20257801021 | |
| Published online | 08 September 2025 | |
- Asad, A. S., Dario, P., Francesco, B.: ‘Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning.’ Autonomous Robots (2022), 46: 483–498 [Google Scholar]
- Tang, G. Z., Neelesh, K., Raymond, Y., Konstantinos, P. M.: ‘Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control.’ Rutgers University. Proceedings of Machine Learning Research. Proceedings of the 2020 Conference on Robot Learning. 2020 [Google Scholar]
- Nicklas, H., Su, H., Wang, X. L.: ‘TD-MPC2: Scalable, Robust World Models for Continuous Control.’ ICLR 2024. 2024 [Google Scholar]
- Liu, Y. T., and Aamir, A.: ‘Multi-Task Reinforcement Learning in Continuous Control with Successor Feature-Based Concurrent Composition.’ IEEE 25-28 June 2024 [Google Scholar]
- Chen, H. Y., Zheng, K. W., Su, H., Zhu J.: ‘Aligning Diffusion Behaviors with Q-functions for Efficient Continuous Control.’ NeurIPS. 2024 [Google Scholar]
- Luo, J. Z., Chen, J. Y., Zhang Q.: ‘Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control.’ Machine Learning (cs.LG); Artificial Intelligence (cs.AI) 16 Oct 2024 [Google Scholar]
- Fang, Z. Y.: ‘Research on Train Operation Control Strategy Based on Deep Reinforcement Learning’, Dalian Jiaotong University. 2023 [Google Scholar]
- Zhang, W. G.: ‘Research on indoor mobile robot navigation based on deep reinforcement learning’, Southeast University. 2021 [Google Scholar]
- Hou, X. H.: ‘Research on Obstacle Avoidance of Field Robots Based on Deep Reinforcement Learning.’ Northeastern University, 2020 [Google Scholar]
- Pang, L. Z.: ‘Research on Key Technologies of Follow up Robots Based on Deep Learning’ Northeastern University. 2019 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.