From Pixels to Temporal Correlations: Learning Informative Representations for Reinforcement Learning Pre-training
从像素到时间相关性:为强化学习预训练学习信息丰富的表示
机构 * School of Computer Science & Technology, Beijing Jiaotong University(北京交通大学计算机科学与技术学院) ; Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing Jiaotong University(北京交通大学交通数据挖掘与具身智能北京市重点实验室) ; Beijing Jiaotong University(北京交通大学)
AI总结 提出多尺度时间对比学习(MTCL)方法,通过建模时间相关性平衡像素空间中各元素的注意力,学习信息更丰富的表示,提升下游强化学习任务的样本效率和性能。
Comments 10 pages, 8 figures. Accepted by ACM MM 2025
Journal ref Proceedings of the 33rd ACM International Conference on Multimedia (MM '25), October 27--31, 2025, Dublin, Ireland