Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 7 | |
| Section | Deep Learning and Reinforcement Learning – Theories and Applications | |
| DOI | https://doi.org/10.1051/itmconf/20257801004 | |
| Published online | 08 September 2025 | |
- J. M. Richards and J. J. Gross, "Emotion regulation and memory: The cognitive costs of keeping one's cool," Journal of Personality and Social Psychology, vol. 79, no. 3, pp. 410, 2000. [Google Scholar]
- J. K. Burgoon, V. Manusov, and L. K. Guerrero, Nonverbal Communication, Routledge, 2021. [Google Scholar]
- M. Hartwig and C. F. Bond, Jr. "Why do lie-catchers fail? A lens model meta-analysis of human lie judgments," Psychological Bulletin, vol. 137, no. 4, pp. 643, 2011. [Google Scholar]
- D. Matsumoto and H. C. Hwang, "Cultural similarities and differences in emblematic gestures," Journal of Nonverbal Behavior, vol. 37, pp. 1–27, 2013. [Google Scholar]
- K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, "Deep reinforcement learning: A brief survey," IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 26–38, 2017. [CrossRef] [Google Scholar]
- V. Mnih et al., "Human-level control through deep reinforcement learning," Nature, vol. 518, no. 7540, pp. 529–533, 2015. [NASA ADS] [CrossRef] [Google Scholar]
- M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele, "2D human pose estimation: New benchmark and state of the art analysis," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3686–3693, 2014. [Google Scholar]
- V. Mnih, "Playing Atari with deep reinforcement learning," arXiv preprint arXiv:1312.5602, 2013. [Google Scholar]
- C. J. Watkins and P. Dayan, "Q-learning," Machine Learning, vol. 8, pp. 279–292, 1992. [Google Scholar]
- L. J. Lin, "Self-improving reactive agents based on reinforcement learning, planning, and teaching," Machine Learning, vol. 8, pp. 293–321, 1992. [Google Scholar]
- J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, "Proximal policy optimization algorithms," arXiv preprint arXiv:1707.06347, 2017. [Google Scholar]
- R. S. Sutton, D. McAllester, S. Singh, and Y. Mansour, "Policy gradient methods for reinforcement learning with function approximation," in Advances in Neural Information Processing Systems, vol. 12, 1999. [Google Scholar]
- J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz, "Trust region policy optimization," in International Conference on Machine Learning, pp. 1889–1897, 2015. [Google Scholar]
- V. Mnih, "Asynchronous methods for deep reinforcement learning," arXiv preprint arXiv:1602.01783, 2016. [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.