1. 9.1 A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
   • Authors: Heyang Zhao, Jiafan He, Quanquan Gu
   • Reason: Addresses fundamental RL challenge with minimax optimal regret and near-optimal switching cost. The authors’ academic and research standing in machine learning communities indicates high credibility and potential influence on the field.
2. 8.9 Reinforcement Learning for Wildfire Mitigation in Simulated Disaster Environments
   • Authors: Alexander Tapley, Marissa Dotter, Michael Doyle, Aidan Fennelly, Dhanuj Gandikota, Savanna Smith, Michael Threet, Tim Welsh
   • Reason: Accepted for a workshop at NeurIPS 2023, tackles a significant real-world problem (wildfire mitigation), strong interdisciplinary relevance, and provides a publicly available system.
3. 8.7 Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning
   • Authors: Changyu Chen, Ramesha Karunasena, Thanh Hong Nguyen, Arunesh Sinha, Pradeep Varakantham
   • Reason: Accepted in NeurIPS 2023, indicating strong peer recognition, and deals with a complex RL challenge involving large discrete action spaces and validity constraints.
4. 8.7 Replay across Experiments: A Natural Extension of Off-Policy RL
   • Authors: Dhruva Tirumala, Thomas Lampe, Jose Enrique Chen, Tuomas Haarnoja, Sandy Huang, Guy Lever, Ben Moran, Tim Hertweck, Leonard Hasenclever, Martin Riedmiller, Nicolas Heess, Markus Wulfmeier
   • Reason: Contributes to the efficiency and performance of RL algorithms, shown benefits across various algorithms and control domains, and includes authors with known contributions to the RL field.
5. 8.6 Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines
   • Authors: Yu-An Lin, Chen-Tao Lee, Guan-Ting Liu, Pu-Jen Cheng, Shao-Hua Sun
   • Reason: Innovative approach integrating programmatic RL with state machines, showing outperformance on various tasks with the potential for generalization to longer horizons.
6. 8.5 Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
   • Authors: Jiachen Li, David Isele, Kanghoon Lee, Jinkyoo Park, Kikuo Fujimura, Mykel J. Kochenderfer
   • Reason: Focus on explainable DRL in a challenging domain of autonomous navigation, integration of novel auxiliary tasks that provides explainable indicators, contribution from authors affiliated with reputable institutions in the field.
7. 8.4 RoboGPT: an intelligent agent of making embodied long-term decisions for daily instruction tasks
   • Authors: Yaran Chen, Wenbo Cui, Yuanwen Chen, Mining Tan, Xinyao Zhang, Dongbin Zhao, He Wang
   • Reason: Integrates large language models with robotic planning, showcasing novel insights into generalization and rationality in planning tasks.
8. 8.4 Should We Learn Most Likely Functions or Parameters?
   • Authors: Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson
   • Reason: Addresses a fundamental question in machine learning with relevance to RL, offers insights on function-space MAP estimation with potential implications for improving generalization and robustness.
9. 8.0 Utilizing Explainability Techniques for Reinforcement Learning Model Assurance
   • Authors: Alexander Tapley, Kyle Gatesman, Luis Robaina, Brett Bissey, Joseph Weissman
   • Reason: Contributes to the crucial aspect of model assurance via explainability, enhancing trust and adoption in real-world applications.
10. 7.8 FRAC-Q-Learning: A Reinforcement Learning with Boredom Avoidance Processes for Social Robots
    • Authors: Akinari Onishi
    • Reason: Proposes a novel method to combat user boredom in social robots, an emerging area in RL applications, with practical implications in human-robot interaction.