1. 9.9 Heterogeneous Multi-Agent Reinforcement Learning via Mirror Descent Policy Optimization
   • Authors: Mohammad Mehdi Nasiri, Mansoor Rezghi
   • Reason: The authors present an innovative approach for cooperative Multi-Agent Reinforcement Learning where agents have diverse capabilities and individual policies. They demonstrate its superiority over other state-of-the-art algorithms indicating the high potential of the proposed method in solving complex problems in the field.
2. 9.5 Value-Distributional Model-Based Reinforcement Learning
   • Authors: Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters
   • Reason: This paper directly addresses reinforcement learning and quantifying uncertainty about a policy’s long-term performance, a critical issue in the field. The authors’ credentials and affiliations also lend significant authority.
3. 9.3 Reinforcement Graph Clustering with Unknown Cluster Number
   • Authors: Yue Liu, Ke Liang, Jun Xia, Xihong Yang, Sihang Zhou, Meng Liu, Xinwang Liu, Stan Z. Li
   • Reason: In this study, the problem of graph clustering using Reinforcement Learning without the guidance of predefined cluster number is addressed. The proposed method demonstrates high effectiveness and efficiency.
4. 9.2 CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning
   • Authors: Jumman Hossain, Abu-Zaher Faridee, Nirmalya Roy, Anjan Basak, Derrik E. Asher
   • Reason: The paper presents a novel approach, using reinforcement learning for navigation planning in an unstructured offroad environment. This could have major implications in the robotics and autonomous vehicle fields.
5. 9.1 Generating observation guided ensembles for data assimilation with denoising diffusion probabilistic model
   • Authors: Yuuichi Asahi, Yuta Hasegawa, Naoyuki Onodera, Takashi Shimokawabe, Hayato Shiba, Yasuhiro Idomura
   • Reason: The authors present an ensemble data assimilation method which shows better performance than established methods especially when the simulation model is imperfect.
6. 9.0 Accelerating Diffusion-based Combinatorial Optimization Solvers by Progressive Distillation
   • Authors: Junwei Huang, Zhiqing Sun, Yiming Yang
   • Reason: The paper suggests a novel method of leveraging machine learning for combinatorial optimization problems, which falls under the broader umbrella of reinforcement learning. The speed improvements promised by their methods can result in more efficient algorithm performance.
7. 9.0 Insurance pricing on price comparison websites via reinforcement learning
   • Authors: Tanut Treetanthiploet, Yufei Zhang, Lukasz Szpruch, Isaac Bowers-Barnard, Henrietta Ridley, James Hickey, Chris Pearce
   • Reason: This paper applies reinforcement learning to a commercially relevant problem of insurance pricing. The innovative integration of model-based and model-free methods and testing against actual industry benchmarks contributes towards the higher influence score.
8. 8.8 A Sequential Meta-Transfer (SMT) Learning to Combat Complexities of Physics-Informed Neural Networks: Application to Composites Autoclave Processing
   • Authors: Milad Ramezankhani, Abbas S. Milani
   • Reason: Though not explicitly about reinforcement learning, this paper addresses advanced learning techniques that may contribute to a better understanding of the generalizability and adaptability issues within neural networks, which could imply to reinforcement learning.
9. 8.8 Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads
   • Authors: Dingheng Mo, Fanchao Chen, Siqiang Luo, Caihua Shan
   • Reason: This paper offers a significant application of reinforcement learning to managing dynamic data workloads, a ubiquitous problem in modern computing systems. The paper’s technical breadth, including a new LSM-tree design, increases its potential influence.
10. 8.6 Generalizing Topological Graph Neural Networks with Paths
    • Authors: Quang Truong, Peter Chin
    • Reason: The authors propose topological graph neural networks with an emphasis on paths and this is found to be more general when compared to similar methods and is shown to produce state-of-the-art results on benchmark tests.
11. 8.6 Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach
    • Authors: Sijie He, Ziye Jia, Chao Dong, Wei Wang, Yilu Cao, Yang Yang, Qihui Wu
    • Reason: The paper presents an intriguing use of reinforcement learning in the field of network management, particularly in terms of security and UAV applications. The results demonstrate improved performance over traditional methods.
12. 8.5 LadleNet: Translating Thermal Infrared Images to Visible Light Images Using A Scalable Two-stage U-Net
    • Authors: Tonghui Zou
    • Reason: Despite the article does not directly focus on reinforcement learning, the application of the advanced model for infrared and visible light image translation could provide insights and techniques relevant to reinforcement learning within the domain of vision-based systems.
13. 8.5 gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling
    • Authors: Aleksandr Petrov, Craig Macdonald
    • Reason: Addressing the problem of overconfidence in recommendation models, this paper introduces a novel loss function and improved model. Its influence stems from its practical implications in improving recommendation systems’ performance.
14. 8.3 Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Optimization for Few-shot Learning
    • Authors: Chengzhengxu Li, Xiaoming Liu, Yichen Wang, Duyi Li, Yu Lan, Chao Shen
    • Reason: This paper presents an innovative approach to optimize discrete prompts using a reinforcement learning framework. It reports significant improvements over the state-of-the-art method on different datasets, which confirms its potential influence in the field of natural language processing and few-shot learning.
15. 8.2 MDB: Interactively Querying Datasets and Models
    • Authors: Aaditya Naik, Adam Stein, Yinjun Wu, Eric Wong, Mayur Naik
    • Reason: This work is about a debugging framework for interactively querying datasets and models which would be important for developers working with AI models.