1. 9.4 Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
   • Authors: Jiechuan Jiang, Kefan Su, Zongqing Lu
   • Reason: The paper serves as a comprehensive survey in an emerging and challenging area of fully decentralized multi-agent reinforcement learning, which indicates high relevance for future research and applications.
2. 9.2 Experiment Planning with Function Approximation
   • Authors: Aldo Pacchiano, Jonathan N. Lee, Emma Brunskill
   • Reason: The paper addresses a novel approach to experiment planning with function approximation in contextual bandit problems, and since one of the authors, Emma Brunskill, is recognized for her contributions to reinforcement learning and its educational applications, the paper is potentially influential.
3. 9.1 Taming “data-hungry” reinforcement learning? Stability in continuous state-action spaces
   • Authors: Yaqi Duan, Martin J. Wainwright
   • Reason: Given the authors’ insights into reinforcement learning in continuous spaces and fast rates of convergence, this paper could influence theoretical understanding and practical applications, especially when considering Martin J. Wainwright’s authority in statistical machine learning.
4. 9.0 ReACT: Reinforcement Learning for Controller Parametrization using B-Spline Geometries
   • Authors: Thomas Rudolf, Daniel Flögel, Tobias Schürmann, Simon Süß, Stefan Schwab, Sören Hohmann
   • Reason: This paper is highly relevant to industrial control applications and provides a novel approach for controller parametrization using deep reinforcement learning, which is a significant contribution to the RL applications in real-world systems.
5. 8.9 AUTOACT: Automatic Agent Learning from Scratch via Self-Planning
   • Authors: Shuofei Qiao, Ningyu Zhang, Runnan Fang, Yujie Luo, Wangchunshu Zhou, Yuchen Eleanor Jiang, Chengfei Lv, Huajun Chen
   • Reason: The paper presents a novel framework, AutoAct, for automatic agent learning without large-scale annotated data, which is a valuable contribution considering the push towards data-efficient learning in AI. The involvement of a comprehensive suite of experiments further boosts its potential impact.