1. 9.8 Causal Reinforcement Learning: A Survey
   • Authors: Zhihong Deng, Jing Jiang, Guodong Long, Chengqi Zhang
   • Reason: The authors have a solid background and reputation in the field. The paper tackles the emerging yet crucial intersection of reinforcement learning with causality, providing a comprehensive review of the literature and paving the way for future research directions.
2. 9.3 Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach
   • Authors: Iman Sharifi, Mustafa Yildirim, Saber Fallah
   • Reason: The paper tackles a critical challenge in the application of deep reinforcement learning in real world scenarios focusing on autonomous driving. The use of first-order symbolic logics for safety in the proposed model is key here.
3. 9.2 Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensing
   • Authors: Qiulei Wang, Lei Yan, Gang Hu, Wenli Chen, Jean Rabault, Bernd R. Noack
   • Reason: Significant improvement in DRL performance, applicability to real-world control tasks, and solid grounding in reinforcement learning methodology.
4. 9.0 Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning
   • Authors: Zhuoran Li, Ling Pan, Longbo Huang
   • Reason: The paper presents a novel approach to offline Multi-Agent Reinforcement Learning, providing significant improvements in performance, generalization and data-efficiency over existing methods.
5. 8.9 Deep Attention Q-Network for Personalized Treatment Recommendation
   • Authors: Simin Ma, Junghwan Lee, Nicoleta Serban, Shihao Yang
   • Reason: The paper focuses on the important area of personalized medicine, proposing a transformative neural network model that can lead to better healthcare outcomes.
6. 8.9 Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
   • Authors: Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey, Emma Brunskill
   • Reason: Innovative approach to simple regret minimization, flexible application to a range of stochastic contextual bandit settings, and insightful negative result on regret guarantees.
7. 8.7 Fast Optimal Transport through Sliced Wasserstein Generalized Geodesics
   • Authors: Guillaume Mahey, Laetitia Chapel, Gilles Gasso, Clément Bonet, Nicolas Courty
   • Reason: This paper presents a new algorithm for faster calculation of the Wasserstein distance, which is crucial for reinforcement learning algorithms. Incremental improvements like these often have great impact.
8. 8.7 DiffFlow: A Unified SDE Framework for Score-Based Diffusion Models and Generative Adversarial Networks
   • Authors: Jingwei Zhang, Han Shi, Jincheng Yu, Enze Xie, Zhenguo Li
   • Reason: Proposes a unified theoretic framework for SDMs and GANs, with potential for new algorithms achieving a better balance between high sample quality and sampling speed.
9. 8.5 Personalized Federated Learning via Amortized Bayesian Meta-Learning
   • Authors: Shiyu Liu, Shaogao Lv, Dun Zeng, Zenglin Xu, Hui Wang, Yue Yu
   • Reason: Offers a novel approach to personalized federated learning and demonstrates superior performance over competitive baselines in experimental results.
10. 8.3 Meta-Learning Adversarial Bandit Algorithms
    • Authors: Mikhail Khodak, Ilya Osadchiy, Keegan Harris, Maria-Florina Balcan, Kfir Y. Levy, Ron Meir, Zhiwei Steven Wu
    • Reason: First to address online meta-learning with bandit feedback, providing theoretical insights and practical algorithms for MAB and BLO.