1. 9.5 Seeing-Eye Quadruped Navigation with Force Responsive Locomotion Control
   • Authors: David DeFazio, Eisuke Hirota, Shiqi Zhang
   • Reason: This paper outlines an innovative technology that uses Reinforcement Learning (RL) to improve the functionality of seeing-eye robots, a highly impactful real-world application. The authors are experienced in this field and have presented their work in CoRL 2023.
2. 9.3 Sample-Efficient Co-Design of Robotic Agents Using Multi-fidelity Training on Universal Policy Network
   • Authors: Kishan R. Nagiredla, Buddhika L. Semage, Thommen G. Karimpanal, Arun Kumar A. V, Santu Rana
   • Reason: The paper introduces a novel approach for co-design optimization which can potentially transform reinforcement learning tasks in many areas besides the author’s focus on robotics.
3. 9.3 Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning
   • Authors: David Yunis, Justin Jung, Falcon Dai, Matthew Walter
   • Reason: The paper focuses on a novel approach to skill-generation to enhance exploration in the sparse-reward reinforcement learning scenario – a significant challenge in the RL field, making it potentially influential.
4. 9.2 Emergent learning in physical systems as feedback-based aging in a glassy landscape
   • Authors: Vidyesh Rao Anisetti, Ananth Kandala, J. M. Schwarz
   • Reason: This paper’s idea of discerning the physical properties of linear physical networks using reinforcement learning stands out as a new approach to understanding learning behaviours of such systems, indicating potential influential scope for its application in various systems.
5. 9.1 Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity
   • Authors: Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He
   • Reason: The paper brings notable improvements in sample complexity in zero-sum LQ games which could be impactful in reinforcement learning research.
6. 9.1 Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach
   • Authors: Sofiane Ouaari, Ali Burak Ünal, Mete Akgün, Nico Pfeifer
   • Reason: The paper provides an interesting perspective on the privacy-utility trade-off in machine learning, a prominent issue in the modern data-reliant industries. Though not directly related to reinforcement learning, it can indirectly influence the field by providing secure data usage methodology.
7. 8.9 Actor critic learning algorithms for mean-field control with moment neural networks
   • Authors: Huyên Pham, Xavier Warin
   • Reason: The presented algorithm could be influential in the field of reinforcement learning by providing a new approach to solve mean-field control problems.
8. 8.9 Parallel and Limited Data Voice Conversion Using Stochastic Variational Deep Kernel Learning
   • Authors: Mohamadreza Jafaryani, Hamid Sheikhzadeh, Vahid Pourahmadi
   • Reason: The paper demonstrates a novel method for voice conversion that performs well on limited data, a persistent challenge in machine learning and deep learning applications. Although not directly related to reinforcement learning, the approach may influence how data limitations are handled in the RL domain.
9. 8.7 Online Submodular Maximization via Online Convex Optimization
   • Authors: T. Si-Salem, G. Özcan, I. Nikolaou, E. Terzi, S. Ioannidis
   • Reason: The paper successfully reduces the task of submodular function optimization to online convex optimization, offering a potential impact on submodular function optimization problems in machine learning.
10. 8.5 Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts
    • Authors: Erik Daxberger, Floris Weers, Bowen Zhang, Tom Gunter, Ruoming Pang, Marcin Eichner, Michael Emmersberger, Yinfei Yang, Alexander Toshev, Xianzhi Du
    • Reason: Authors introduce a way to optimize Vision Transformers for mobile usage without sacrificing effectiveness, which could be utilized for application with limitations in resources.