1. 9.5 Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance
   • Authors: Yuchen Fang, Zhenggang Tang, Kan Ren, Weiqing Liu, Li Zhao, Jiang Bian, Dongsheng Li, Weinan Zhang, Yong Yu, Tie-Yan Liu
   • Reason: This paper is influential due to its integration of multi-agent reinforcement learning for multi-order execution in finance, an area of high practical importance. Additionally, its advanced communication protocol for agent interaction demonstrates potential for widespread utility.
2. 9.3 Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation
   • Authors: Yu Chen, Yihan Du, Pihe Hu, Siwei Wang, Desheng Wu, Longbo Huang
   • Reason: This paper addresses the much-needed safety considerations in reinforcement learning by proposing an algorithm for risk-sensitive RL with function approximation. Its novel techniques and mathematical rigor make it highly influential.
3. 9.2 Offline Reinforcement Learning with Imbalanced Datasets
   • Authors: Li Jiang, Sijie Chen, Jielin Qiu, Haoran Xu, Wai Kin Chan, Zhao Ding
   • Reason: This paper brings attention to an important and understudied aspect of RL, i.e., the effect of imbalanced datasets. The paper not only identifies the issues but also proposes a method to address the imbalance, making it impactfully influential.
4. 9.1 Hierarchical Empowerment: Towards Tractable Empowerment-Based Skill-Learning
   • Authors: Andrew Levy, Sreehari Rammohan, Alessandro Allievi, Scott Niekum, George Konidaris
   • Reason: This paper introduces Hierarchical Empowerment, a new framework that effectively deals with the challenge of learning large skill sets. The ability to learn skills to cover a larger surface area as demonstrated makes it significant and potentially influential.
5. 9.0 Stability of Q-Learning Through Design and Optimism
   • Authors: Sean Meyn
   • Reason: This paper dives deep into the fundamental concepts of Q-learning and proposes new approaches to ensure stability in these algorithms. Considering the foundational knowledge it contributes on stability of Q-Learning with linear function approximation, it is set to be a highly influential paper.