1. 9.4 Feedback is All You Need: Real-World Reinforcement Learning with Approximate Physics-Based Models
   • Authors: Tyler Westenbroek, Jacob Levy, David Fridovich-Keil
   • Reason: This study demonstrates an innovative approach to improving reinforcement learning efficiency for robots through real-world data, and the authors have strong background in robotics which may increase its impact.
2. 9.3 Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty
   • Authors: Guanin Liu, Zhihan Zhou, Han Liu, Lifeng Lai
   • Reason: The authors focus on action robust RL with probabilistic policy execution uncertainty, providing a significant contribution to the research in RL by proposing an optimal policy on the action robust MDPs with probabilistic policy execution uncertainty. It also shows to outperform non-robust RL algorithms.
3. 9.2 Improved Self-Normalized Concentration in Hilbert Spaces: Sublinear Regret for GP-UCB
   • Authors: Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas
   • Reason: The authors propose a model to improve the regret rates of existing kernel bandit problems using a Gaussian Process Upper Confidence Bound (GP-UCB) algorithm, which could resolve the problem of suboptimal GP-UCB regret rate, thus expanding the potential applications of kernelized bandit problems in various fields.
4. 9.2 Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning
   • Authors: L. L. Ankile, B. S. Ham, K. Mao, E. Shin, S. Swaroop, F. Doshi-Velez, W. Pan
   • Reason: This paper explores the connections between user traits and behaviors in reinforcement learning, which contributes to the personalization of interventions, a hot topic in today’s ML applications.
5. 9.0 Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?
   • Authors: Linfeng Zhao, Owen Howell, Jung Yeon Park, Xupeng Zhu, Robin Walters, Lawson L.S. Wong
   • Reason: The paper provides valuable insights into utilizing Euclidean group symmetry in reinforcement learning and planning tasks, and its novel proposition of planning algorithms can also affect RL applications in robotic tasks.
6. 8.8 Learning Multiple Coordinated Agents under Directed Acyclic Graph Constraints
   • Authors: Jaeyeon Jang, Diego Klabjan, Han Liu, Nital S. Patel, Xiuqi Li, Balakrishnan Ananthanarayanan, Husam Dauod, Tzung-Han Juang
   • Reason: The authors propose a novel strategy to enhance the performance of Multi-Agent Reinforcement Learning (MARL) by exploiting the Directed Acyclic Graph (DAG) structure between agents, which shows potential for improving RL for complex multi-agent systems.
7. 8.7 RAYEN: Imposition of Hard Convex Constraints on Neural Networks
   • Authors: Jesus Tordesillas, Jonathan P. How, Marco Hutter
   • Reason: The paper introduces an effective method to impose hard convex constraints on neural networks without any sacrifice of computational efficiency.
8. 8.5 Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning
   • Authors: Guanlin Liu, Lifeng Lai
   • Reason: The authors provide an analysis of the impact of adversarial attacks on MARL models proposing a mixed attack strategy that can efficiently compromise MARL agents. This could be important for further hardening and improvement of RL algorithms.
9. 8.5 A Multiobjective Reinforcement Learning Framework for Microgrid Energy Management
   • Authors: M. Vivienne Liu, Patrick M. Reed, David Gold, Garret Quist, C. Lindsay Anderson
   • Reason: The paper investigates how multi-objective reinforcement learning can optimize microgrid energy management, which is a crucial issue in the era of sustainable energy.
10. 8.1 On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms
    • Authors: Yinglun Xu, Bhuvesh Kumar, Jacob Abernethy
    • Reason: This paper brings up a significant highlight on the robustness of the ε-greedy algorithm to adversarial data corruption, and extends its application in a contextual multi-arm bandit mechanism setting, thus providing valuable insight in making bandit algorithms more robust.