arXiv AI By Tony Wang, Qian Yang

Exploring Reinforcement Learning for Fluid Transitions Between Clinical Mental Healthcare and Everyday Wellness Support

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arXiv:2606. 06800v1 Announce Type: cross Abstract: Mental health struggles wax and wane, yet clinical and wellness interventions typically operate separately, causing frequent breakdowns at care transitions.

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arXiv Machine Learning
Aug 11

Learning Multi-Timescale Interventions under Safety and Resource Constraints

arXiv:2508. 03875v2 Announce Type: replace Abstract: Many sequential decision problems offer qualitatively different ways of influencing the environment: some interventions act immediately, whereas others induce persistent effects that continue to shape future states long after the decision that initiated them.

By David Mguni, Wanrong Yang, Jing Dong, Ziquan Liu, Muhammad Salman Haleem, Baoxiang Wang, Dominik Wojtczak
arXiv Machine Learning
Jul 14

Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions

arXiv:2601. 15353v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has achieved remarkable success in real-world decision-making across diverse domains, including gaming, robotics, online advertising, public health, and natural language processing.

By Asim H. Gazi, Yongyi Guo, Daiqi Gao, Ziping Xu, Kelly W. Zhang, Susan A. Murphy
arXiv Machine Learning
Jun 2

MedGym:A Unified Continuous-Time Benchmark for Dynamic Medical Treatment Reinforcement Learning

arXiv:2606. 01028v1 Announce Type: new Abstract: Medical treatment recommendation poses several challenges to reinforcement learning (RL): patient physiology evolves in continuous time, measurements and interventions are performed at irregular intervals, and treatment effects vary substantially across individuals.

By Yuepeng Wang, Ken Kawano, Yongqi Zhou, Yoshihiko Fujisawa, Richard Weiss, Akifumi Wachi, Katsuki Fujisawa, Ying Chen, Mehrshad Sadria, Xin Liu, Kyoung-Sook Kim, Xiao Hu, Sebastien Gros, Xun Shen