1. 9.7 RL$^3$: Boosting Meta Reinforcement Learning via RL inside RL$^2$
   • Authors: Abhinav Bhatia, Samer B. Nashed, Shlomo Zilberstein
   • Reason: The paper introduces a novel approach (RL$^3$), which is a hybrid of traditional RL and meta-RL, and demonstrate its superior performance on long-horizon and out-of-distribution tasks. It also has an influence from Shlomo Zilberstein who is a recognized authority in the AI and ML community.
2. 9.5 Curious Replay for Model-based Adaptation
   • Authors: Isaac Kauvar, Chris Doyle, Linqi Zhou, Nick Haber
   • Reason: This paper tackles the problem of improving model-based RL by incorporating a curiosity-based priority signal. It also shows improved performance in an exploration paradigm and maintains the performance on the Deepmind Control Suite.
3. 9.3 DCT: Dual Channel Training of Action Embeddings for Reinforcement Learning with Large Discrete Action Spaces
   • Authors: Pranavi Pathakota, Hardik Meisheri, Harshad Khadilkar
   • Reason: The paper presents a novel framework to learn action embeddings that can help in scenarios with a large number of possible actions. It’s also tested on real-world e-commerce transaction data and a 2D maze environment.
4. 9.2 Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning
   • Authors: Xinyang Lu, Flint Xiaofeng Fan, Tianying Wang
   • Reason: This work proposes an RL method for action and trajectory planning in complex urban driving scenarios. The algorithm handles multiple driving tasks and dynamically evolving environment which provides more practical scenarios.
5. 9.1 Rethinking Closed-loop Training for Autonomous Driving
   • Authors: Chris Zhang, Runsheng Guo, Wenyuan Zeng, Yuwen Xiong, Binbin Dai, Rui Hu, Mengye Ren, Raquel Urtasun
   • Reason: This paper is most likely to have significant impact as it tackles a central problem in reinforcement learning and autonomous driving, and it proposes a new learning algorithm. Furthermore, the paper is from ECCV, a prestigious conference, and the last author is a well-known expert in the field of autonomous driving.
6. 9.0 A Population-Level Analysis of Neural Dynamics in Robust Legged Robots
   • Authors: Eugene R. Rush, Christoffer Heckman, Kaushik Jayaram, J. Sean Humbert
   • Reason: This paper makes significant contributions to the understanding of recurrent neural network-based reinforcement learning systems and their applications in robotic control. The study offers novel insights which could have a huge impact on the future of robot locomotion controllers.
7. 9.0 Structure in Reinforcement Learning: A Survey and Open Problems
   • Authors: Aditya Mohan, Amy Zhang, Marius Lindauer
   • Reason: This paper provides a comprehensive survey and framework analyzing structural aspects in RL, leading to a better understanding of the RL learning process. Such pieces of work usually have a lasting impact as they guide future research directions.
8. 8.9 Hierarchical Graph Neural Networks for Proprioceptive 6D Pose Estimation of In-hand Objects
   • Authors: Alireza Rezazadeh, Snehal Dikhale, Soshi Iba, Nawid Jamali
   • Reason: The novelty of the introduced hierarchical graph neural network and the successful incorporation of multimodal sensory information could make a significant influence on the development of enhanced robotic manipulation systems.
9. 8.7 Imitation with Spatial-Temporal Heatmap: 2nd Place Solution for NuPlan Challenge
   • Authors: Yihan Hu, Kun Li, Pingyuan Liang, Jingyu Qian, Zhening Yang, Haichao Zhang, Wenxin Shao, Zhuangzhuang Ding, Wei Xu, Qiang Liu
   • Reason: This paper presents a new method for achieving safe planning in complex autonomous driving scenarios. The method’s success in a competition adds further credibility, and its wide applicability brings potential for broad influence.
10. 8.5 Quantum Federated Learning: Analysis, Design and Implementation Challenges
    • Authors: Dev Gurung, Shiva Raj Pokhrel, Gang Li
    • Reason: This paper might have potential impact, as it reviews the important field of Quantum Federated Learning. Although it does not propose new algorithms, it develops ideas for new frameworks and offers a high-level viewpoint on future research directions, which could inspire other researchers.