1. 9.7 State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding
   • Authors: Devleena Das, Sonia Chernova, Been Kim
   • This paper presents an innovative framework for generating concept-based explanations for AI decision-making. It targets reinforcing learning towards better performance and user understanding. Backed by the acceptance from NeurIPS 2023, it shows potential to have strong influence in the reinforcement learning field.
2. 8.6 Building explainable graph neural network by sparse learning for the drug-protein binding prediction
   • Authors: Yang Wang, Zanyu Shi, Timothy Richardson, Kun Huang, Pathum Weerawarna, Yijie Wang
   • The authors propose an innovative method for creating explainable Graph Neural Networks (GNNs), which can effectively identify key chemical structures in drug-protein binding. Given its potential impact on drug discovery and development, it is believed to have a high influence.
3. 8.5 Expressive variational quantum circuits provide inherent privacy in federated learning
   • Authors: Niraj Kumar, Jamie Heredge, Changhao Li, Shaltiel Eloul, Shree Hari Sureshbabu, Marco Pistoia
   • The paper proposes the use of variational quantum circuits in federated learning to provide inherent privacy protection against gradient inversion attacks. This novel approach could significantly influence privacy standards in federated learning scenarios, making it highly impactful.
4. 8.4 H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps
   • Authors: Haoyi Niu, Tianying Ji, Bingqi Liu, Haocheng Zhao, Xiangyu Zhu, Jianying Zheng, Pengfei Huang, Guyue Zhou, Jianming Hu, Xianyuan Zhan
   • The research presents an enhanced framework for reinforcement learning that offers a comprehensive solution for policy learning in real-world robotics. Given its application potential in dynamic environments, it is ranked as an influential paper in reinforcement learning.
5. 8.0 Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs Using Reinforcement Learning
   • Authors: Yousef AlSaqabi, Bhaskar Krishnamachari
   • The work puts forward a novel reinforcement learning solution for route planning of autonomous vehicles, focusing on high-speed data transmission needs. With the potential to improve efficiency in the realm of autonomous vehicles, this paper is deemed to be of high impact.