1. 9.9 Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory
   • Authors: Karthik Somayaji NS, Yu Wang, Malachi Schram, Jan Drgona, Mahantesh Halappanavar, Frank Liu, Peng Li
   • Reason: This paper presented a new approach to enhance the resilience of RL agents. It provides substantial theoretical justification and showed successful practical evaluations, making it potentially impactful.
2. 9.5 Racing Towards Reinforcement Learning based control of an Autonomous Formula SAE Car
   • Authors: Aakaash Salvaji, Harry Taylor, David Valencia, Trevor Gee, Henry Williams
   • Reason: The paper focuses on applying Deep Reinforcement Learning for end-to-end control of an autonomous race car - a popular and complex application. It’s accepted at the Australasian Conference on Robotics and Automation (ARCA 2022)
3. 9.3 Nonparametric Additive Value Functions: Interpretable Reinforcement Learning with an Application to Surgical Recovery
   • Authors: Patrick Emedom-Nnamdi, Timothy R. Smith, Jukka-Pekka Onnela, Junwei Lu
   • Reason: This work proposed a new method for estimating value functions in reinforcement learning, aiming towards making reinforcement learning more interpretable and applicable in medical practice.
4. 8.7 Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems
   • Authors: Tianchi Cai, Shenliao Bao, Jiyan Jiang, Shiji Zhou, Wenpeng Zhang, Lihong Gu, Jinjie Gu, Guannan Zhang
   • Reason: The paper addresses a specific challenge in reinforcement learning-based recommenders, providing two frameworks to improve the performance of these systems.
5. 8.5 Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-Diversity
   • Authors: Achkan Salehi, Stephane Doncieux
   • Reason: This paper makes a combination of a Large Language Model (LLM) and a policy that can be shaped by high-level textual prompts, promoting more usable, understandable reinforcement learning algorithm.