1. 9.2 Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e
   • Authors: Chenwei Xu, Jerry Yao-Chieh Hu, Aakaash Narayanan, Mattson Thieme, Vladimir Nagaslaev, Mark Austin, Jeremy Arnold, Jose Berlioz, Pierrick Hanlet, Aisha Ibrahim, Dennis Nicklaus, Jovan Mitrevski, Jason Michael St.John, Gauri Pradhan, Andrea Saewert, Kiyomi Seiya, Brian Schupbach, Randy Thurman-Keup, Nhan Tran, Rui Shi, Seda Ogrenci, Alexis Maya-Isabelle Shuping, Kyle Hazelwood, Han Liu
   • Reason: The paper was accepted at a prestigious workshop (NeurIPS 2023 ML4Phy Workshop) and tackles a real-world problem with significant improvements over traditional methods, indicating a high impact on both the domain of physics and reinforcement learning.
2. 8.9 Adaptive Control Strategy for Quadruped Robots in Actuator Degradation Scenarios
   • Authors: Xinyuan Wu, Wentao Dong, Hang Lai, Yong Yu, Ying Wen
   • Reason: The paper introduces a reinforcement learning framework for a very practical use-case in robotics with empirical validations, showing potential for influencing both the RL and robotics communities.
3. 8.7 Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach
   • Authors: Ramin Giahi, Cameron A. MacKenzie, Reyhaneh Bijari
   • Reason: The paper applies Deep Q-learning to the relevant field of engineering system design in presence of uncertainties, a problem with broad applications, suggesting it could be influential in the RL application domain.
4. 8.5 Design Space Exploration of Approximate Computing Techniques with a Reinforcement Learning Approach
   • Authors: Sepide Saeedi, Alessandro Savino, Stefano Di Carlo
   • Reason: It applies RL to the important field of approximate computing, presenting a multi-objective exploration strategy that could affect various performance-critical domains.
5. 8.3 Data Augmentation for Supervised Graph Outlier Detection with Latent Diffusion Models
   • Authors: Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, Philip S. Yu
   • Reason: Tackling the issue of class imbalance in graph outlier detection using generative models, this paper presents a novel approach with potential implications for both machine learning and network analysis.