1. 9.3 Episodic Reinforcement Learning with Expanded State-reward Space
   • Authors: Dayang Liang, Yaru Zhang, Yunlong Liu
   • Reason: The authors address a critical issue in the data efficiency of DRL, proposing a new EC-based framework integrating historical information which potentially enhances the accuracy of value estimation and policy performance, thereby potentially substantially impacting the reinforcement learning field.
2. 9.2 Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model
   • Authors: Yinan Zheng, Jianxiong Li, Dongjie Yu, Yujie Yang, Shengbo Eben Li, Xianyuan Zhan, Jingjing Liu
   • Reason: The paper introduces a novel approach for satisfying hard safety constraints in offline RL, critical for safety-critical applications, and presents the FISOR framework, which is supported by reachability analysis from safe-control theory, indicating high potential influence in both theory and practice.
3. 9.0 Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring Systems
   • Authors: Ziqiaing Ye, Yulan Gao, Yue Xiao, Zehui Xiong, Dusit Niyato
   • Reason: The paper proposes a novel DRL-based health monitoring system that dynamically adapts to user activity, which could significantly influence smart healthcare by optimizing performance and resource efficiency.
4. 8.9 Cooperative Multi-Agent Graph Bandits: UCB Algorithm and Regret Analysis
   • Authors: Phevos Paschalidis, Runyu Zhang, Na Li
   • Reason: The paper addresses a novel multi-agent environment with a focus on cooperative strategies and UCB-based learning algorithms, presenting a significant theoretical contribution with regret analysis, which is central to progress in reinforcement learning. The topic is at the forefront of RL research and the authors provide credible expertise.
5. 8.8 Catastrophic Interference is Mitigated in Naturalistic Power-Law Learning Environments
   • Authors: Atith Gandhi, Raj Sanjay Shah, Vijay Marupudi, Sashank Varma
   • Reason: This work investigates a rehearsal-based approach in power-law learning environments for mitigating catastrophic interference, a critical issue in neural networks, showing promise for improvements in continual learning.
6. 8.7 Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs
   • Authors: M. Saeid HaghighiFard, Sinem Coleri
   • Reason: The research introduces an innovative hierarchical federated learning framework for VANETs, a problem of growing importance due to the emergence of smart vehicles and IoT. The approach is particularly relevant for real-world applications, and the author has authority in this specific domain.
7. 8.6 Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
   • Authors: Hong kyu Lee, Qiuchen Zhang, Carl Yang, Jian Lou, Li Xiong
   • Reason: The authors introduce a potentially influential new framework for machine unlearning leveraging contrastive learning, which could be significant for privacy and compliance in machine learning applications.
8. 8.5 Learning Non-myopic Power Allocation in Constrained Scenarios
   • Authors: Arindam Chowdhury, Santiago Paternain, Gunjan Verma, Ananthram Swami, Santiago Segarra
   • Reason: This paper proposes a practical learning-based framework for power allocation in wireless networks, employing reinforcement learning techniques suitable for constrained decision-making. The relevance for real-world scenarios and the inclusion of the authors from a well-regarded conference (ASILOMAR) add to the paper’s potential impact.
9. 8.2 Hacking Predictors Means Hacking Cars: Using Sensitivity Analysis to Identify Trajectory Prediction Vulnerabilities for Autonomous Driving Security
   • Authors: Marsalis Gibson, David Babazadeh, Claire Tomlin, Shankar Sastry
   • Reason: The paper directly contributes to the security aspect of reinforcement learning applications in autonomous driving, a critical area for the deployment of these systems in the real world. The authors have a strong background in the intersection of cybersecurity and machine learning.
10. 8.0 Noise Contrastive Estimation-based Matching Framework for Low-resource Security Attack Pattern Recognition
    • Authors: Tu Nguyen, Nedim Srndic, Alexander Neth
    • Reason: This paper approaches the niche yet vital problem of security attack pattern recognition using a learning paradigm pertinent to reinforcement learning. The methodological novelty and the authors’ experience in the cybersecurity domain render the paper potentially influential.