- 9.3 Global Convergence of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction
- Authors: Jie Feng, Ke Wei, Jinchi Chen
- Reason: The paper introduces a novel variant of Natural Policy Gradient with promising empirical results and a strong theoretical sample complexity result; the authors’ affiliation and the relevance to a core component of reinforcement learning algorithms suggest high potential influence.
- 8.8 Reinforcement Learning for SAR View Angle Inversion with Differentiable SAR Renderer
- Authors: Yanni Wang, Hecheng Jia, Shilei Fu, Huiping Lin, Feng Xu
- Reason: Innovative application of deep reinforcement learning to electromagnetic inverse problems in radar imaging with significant performance improvements demonstrated on both simulated and real datasets.
- 8.5 Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning
- Authors: Mohamad Abed El Rahman Hammoud, Naila Raboudi, Edriss S. Titi, Omar Knio, Ibrahim Hoteit
- Reason: Addresses a significant limitation of ensemble Kalman filters and demonstrates the efficacy of reinforcement learning in a novel context of data assimilation in chaotic systems, with a potential broad impact on various domains.
- 8.2 Towards Model-Free LQR Control over Rate-Limited Channels
- Authors: Aritra Mitra, Lintao Ye, Vijay Gupta
- Reason: Connects model-free control design with networked control systems in the novel setting of rate-limited channels and proposes an algorithm with a strong convergence guarantee, which is a pertinent problem in distributed systems and control theory.
- 7.9 Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
- Authors: Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu
- Reason: Although this paper addresses language models rather than reinforcement learning directly, the concept of self-play is central to reinforcement learning, and their findings could cross-apply, indicating its potential influence on reinforcement learning through improved training techniques.