1. 8.7 De novo Drug Design using Reinforcement Learning with Multiple GPT Agents
   • Authors: Xiuyuan Hu, Guoqing Liu, Yang Zhao, Hao Zhang
   • Reason: High significance in pharmacology with potential impact on SARS-CoV-2 research, accepted by a top-tier conference (NeurIPS), and a strong conceptual combination of reinforcement learning with GPT models which are highly influential in the field.
2. 8.5 Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning
   • Authors: Yaowei Hu, Jacob Lear, Lu Zhang
   • Reason: Addresses a novel aspect of fairness in machine learning within dynamic systems, a topic of growing importance, and demonstrates use of reinforcement learning to balance utility with long-term fairness.
3. 8.3 Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization
   • Authors: Jiahao Qiu, Hui Yuan, Jinghong Zhang, Wentao Chen, Huazheng Wang, Mengdi Wang
   • Reason: Published in a highly reputable conference (AAAI), presents a unique combination of tree search and bandit learning methods in the context of protein engineering, a critical area of research with wide-ranging implications.
4. 8.1 Personalized Reinforcement Learning with a Budget of Policies
   • Authors: Dmitry Ivanov, Omer Ben-Porat
   • Reason: Accepted to AAAI 2024 and introduces an innovative approach to personalization in reinforcement learning, with potential to influence high-stakes fields such as healthcare and autonomous driving which require stringent regulatory approvals.
5. 7.9 Maximum Causal Entropy Inverse Reinforcement Learning for Mean-Field Games
   • Authors: Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi
   • Reason: A substantial paper with deep theoretical contributions to the field, offering a robust methodology for addressing inverse reinforcement learning in complex mean-field games, which could influence both theoretical research and applications in economics and social sciences.