1. 9.5 Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities
   • Authors: Markus Wulfmeier, Arunkumar Byravan, Sarah Bechtle, Karol Hausman, Nicolas Heess
   • Reason: The paper addresses the critical aspect of knowledge transfer and generalization in reinforcement learning, with contributions from recognized researchers in the field, providing a comprehensive taxonomy and guidelines that could influence future research and applications.
2. 9.2 Energy-based Potential Games for Joint Motion Forecasting and Control
   • Authors: Christopher Diehl, Tobias Klosek, Martin Krüger, Nils Murzyn, Timo Osterburg, Torsten Bertram
   • Reason: The paper fuses game-theoretic approaches with machine learning for robotics, which is an innovative approach. Being presented at CoRL 2023, it is likely to reach a relevant audience, which could amplify its impact in the robotics and reinforcement learning community.
3. 9.1 Harnessing Discrete Representations For Continual Reinforcement Learning
   • Authors: Edan Meyer, Marlos C. Machado, Adam White
   • Reason: Building upon recent breakthroughs, this paper presents thorough empirical investigation regarding discrete representations in reinforcement learning, with compelling results demonstrating significant improvements in learning policies. Given the depth of analysis and the reputation of the authors in the reinforcement learning community, such as Adam White, this paper has high potential for influence.
4. 8.9 Nash Learning from Human Feedback
   • Authors: Rémi Munos, Michal Valko, Daniele Calandriello, Mohammad Gheshlaghi Azar, Mark Rowland, Daniel Guo, Yunhao Tang, Matthieu Geist, Thomas Mésnard, Andrea Michi, Marco Selvi, Sertan Girgin, Nikola Momchev, Olivier Bachem, Daniel J. Mankowitz, Doina Precup, Bilal Piot
   • Reason: The concept of Nash learning from human feedback represents a significant paradigm shift with potential industry-wide implications, and the caliber of authors, including Rémi Munos and Doina Precup, both highly respected in the field, adds to the paper’s potential influence.
5. 8.8 Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models
   • Authors: Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl
   • Reason: This paper introduces a zero-shot imitation learning approach which can have a substantial impact on the way learning from observation is conducted, proposed by authors associated with a reputable lab which adds to the authority of the work.
6. 8.7 Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence
   • Authors: Rambod Mojgani, Daniel Waelchli, Yifei Guan, Petros Koumoutsakos, Pedram Hassanzadeh
   • Reason: This paper tackles a highly relevant issue in climate science utilizing an innovative SMARL approach. The potential for real-world impact in climate modeling and prediction could be sizeable, and the authors’ cross-disciplinary effort may spawn new research avenues.
7. 8.6 A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
   • Authors: Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, Laura Toni
   • Reason: The Survey offers a comprehensive review and formal analysis of temporal credit assignment in deep RL. With its extensive and insightful coverage, as well as the participation of noted experts like Hado van Hasselt, it stands to be highly influential for both new and established researchers.
8. 8.5 Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees
   • Authors: Đorđe Žikelić, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee, Thomas A. Henzinger
   • Reason: It addresses a critical gap in reinforcement learning by presenting a method that offers formal behavior guarantees for neural network policies in stochastic environments, a key concern for real-world applications. The involvement of authors with strong track records in both theoretical computer science and machine learning, such as Thomas A. Henzinger, indicates potential for significant impact.
9. 8.4 Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing
   • Authors: Ying Yuan, Haichuan Che, Yuzhe Qin, Binghao Huang, Zhao-Heng Yin, Kang-Won Lee, Yi Wu, Soo-Chul Lim, Xiaolong Wang
   • Reason: The integration of tactile and visual feedback for robotic manipulation is a novel topic that complements the aspects of reinforcement learning concerned with sensory integration; this could have a significant impact on both the robotics and reinforcement learning fields.
10. 7.9 HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning
    • Authors: Xingtong Yu, Zemin Liu, Yuan Fang, Xinming Zhang
    • Reason: While this paper addresses few-shot learning across graph structures, which is not squarely in the typical reinforcement learning domain, the idea intersects with RL in context of learning from limited data. Additionally, it might inspire new strategies in RL that can benefit from graph-based data representation and prompt learning frameworks.