1. 9.4 CycLight: learning traffic signal cooperation with a cycle-level strategy
   • Authors: Gengyue Han, Xiaohan Liu, Xianyue Peng, Hao Wang, Yu Han
   • Reason: Introduces a novel cycle-level deep reinforcement learning approach for traffic signal control, addressing a real-world problem with implications for urban traffic management, co-authored by researchers from institutions known for advancements in machine learning which may indicate a strong authority in the field.
2. 9.2 On Quantum Natural Policy Gradients
   • Authors: André Sequeira, Luis Paulo Santos, Luis Soares Barbosa
   • Reason: Investigates the role of quantum Fisher Information Matrix in reinforcement learning, a cutting-edge topic at the intersection of quantum computing and RL. Authors from institutions with notable contributions to quantum computing research, implying substantial authority in the domain.
3. 9.0 Reinforcement Learning for Scalable Train Timetable Rescheduling with Graph Representation
   • Authors: Peng Yue, Yaochu Jin, Xuewu Dai, Zhenhua Feng, Dongliang Cui
   • Reason: The paper addresses the practical and challenging problem of train timetable rescheduling with a reinforcement learning approach showing effective results. The novel use of graph representation and extensive experimental results suggest potential for significant impact in the transportation domain.
4. 8.9 Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning
   • Authors: Fatemeh Lotfi, Fatemeh Afghah
   • Reason: This paper is published in a reputable conference and presents an innovative deep reinforcement learning approach with applications to network slice management, and the techniques employed could be influential for future research in 5G networks and beyond.
5. 8.9 Learn What You Need in Personalized Federated Learning
   • Authors: Kexin Lv, Rui Ye, Xiaolin Huang, Jie Yang, Siheng Chen
   • Reason: Addresses data heterogeneity in federated learning with a novel algorithm that allows clients to adaptively select model parameters for collaboration, which is essential for real-world applications like mobile devices and IoT, authored by researchers likely to have a strong background in federated learning and personalized models.
6. 8.8 Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug Design
   • Authors: Mohamed-Amine Chadi, Hajar Mousannif, Ahmed Aamouche
   • Reason: The proposed curiosity-driven method for molecular generation could have a high impact in the pharmaceutical industry, despite being in a very specialized subsection of reinforcement learning.
7. 8.7 Quantum Advantage Actor-Critic for Reinforcement Learning
   • Authors: Michael Kölle, Mohamad Hgog, Fabian Ritz, Philipp Altmann, Maximilian Zorn, Jonas Stein, Claudia Linnhoff-Popien
   • Reason: Integrating quantum computing with reinforcement learning is a state-of-the-art approach that could pave the way for new breakthroughs in RL scalability and performance.
8. 8.7 Transferring Core Knowledge via Learngenes
   • Authors: Fu Feng, Jing Wang, Xin Geng
   • Reason: Proposes a novel concept for knowledge transfer in neural networks inspired by genetic inheritance, which could influence a wide variety of learning tasks, from the authors with a track record in evolutionary computation and neural network research.
9. 8.6 Distance-aware Attention Reshaping: Enhance Generalization of Neural Solver for Large-scale Vehicle Routing Problems
   • Authors: Yang Wang, Ya-Hui Jia, Wei-Neng Chen, Yi Mei
   • Reason: The paper offers an innovative approach to solving large-scale vehicle routing problems, a key challenge in the logistics industry, and the enhancement of neural solvers for generalization could lead to widespread practical implications.
10. 8.5 PRewrite: Prompt Rewriting with Reinforcement Learning
    • Authors: Weize Kong, Spurthi Amba Hombaiah, Mingyang Zhang, Qiaozhu Mei, Michael Bendersky
    • Reason: Discusses automation of prompt engineering for large language models, which can significantly influence NLP applications, by authors potentially authoritative in NLP and machine learning given their association with institutions known for research in these areas.