1. 9.2 Carve3D: Improving Multi-view Reconstruction Consistency for Diffusion Models with RL Finetuning
   • Authors: Desai Xie, Jiahao Li, Hao Tan, Xin Sun, Zhixin Shu, Yi Zhou, Sai Bi, Sören Pirk, Arie E. Kaufman
   • Reason: The paper is accepted to CVPR 2024, a top conference in the computer vision field. Its novel approach to improve multi-view consistency using reinforcement learning finetuning (RLFT) could significantly impact related areas, and it is supported by an extensive list of figures and a publicly available codebase for further research and application.
2. 8.9 Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games
   • Authors: Jing Dong, Baoxiang Wang, Yaoliang Yu
   • Reason: Addresses a fundamental problem in game theory with reinforcement learning implications. It also offers both theoretical advancements and experimental results. The authors’ development of a Frank-Wolfe-based algorithm with proven convergence and regret bounds has the potential to influence strategies in multi-agent systems.
3. 8.7 Best Response Shaping
   • Authors: Milad Aghajohari, Tim Cooijmans, Juan Agustin Duque, Shunichi Akatsuka, Aaron Courville
   • Reason: This paper is authored by researchers affiliated with well-regarded institutions, and potentially adds significant contributions to the field of multi-agent reinforcement learning in partially competitive environments.
4. 8.3 Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles
   • Authors: Jie Wang, Yash Vardhan Pant, Lei Zhao, Michał Antkiewicz, Krzysztof Czarnecki
   • Reason: The paper appears in IEEE Transactions on Intelligent Transportation Systems and addresses the pressing practicality of autonomous vehicle (AV) integration. Their work on Gaussian process-enhanced model predictive control (GP-MPC) offers implementation-ready strategies that might directly influence this rapidly advancing industry.
5. 8.0 Multi-Agent Soft Actor-Critic with Global Loss for Autonomous Mobility-on-Demand Fleet Control
   • Authors: Zeno Woywood, Jasper I. Wiltfang, Julius Luy, Tobias Enders, Maximilian Schiffer
   • Reason: Optimizing control of Autonomous Mobility-on-Demand systems is a relevant challenge in applying reinforcement learning to real-world problems. The significance of this task and the author’s approach, particularly the integration of rebalancing, could make this paper influential within the field of smart cities and transportation.