1. 9.9 Settling the Sample Complexity of Online Reinforcement Learning
   • Authors: Zihan Zhang, Yuxin Chen, Jason D. Lee, Simon S. Du
   • Reason: The paper addresses a fundamental problem in online reinforcement learning and introduces a new method that achieves minimax-optimal regret without any burn-in cost. The approach could potentially revolutionize how reinforcement learning algorithms are designed and evaluated.
2. 9.5 Contrastive Example-Based Control
   • Authors: Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn
   • Reason: The authors propose a new way of handling real-world reinforcement learning problems, which could lead to more robust and scalable RL algorithms.
3. 9.2 Counterfactual Explanation Policies in RL
   • Authors: Shripad V. Deshmukh, Srivatsan R, Supriti Vijay, Jayakumar Subramanian, Chirag Agarwal
   • Reason: The paper tackles the important issue of explainability in reinforcement learning, opening the door for more reliable and accountable AI systems in decision-making roles.
4. 8.9 Deep Reinforcement Learning for Robust Goal-Based Wealth Management
   • Authors: Tessa Bauman, Bruno Gašperov, Stjepan Begušić, Zvonko Kostanjčar
   • Reason: The paper brings reinforcement learning techniques to financial applications, which could impact and potentially revolutionize wealth management practices.
5. 8.7 Unbiased Weight Maximization
   • Authors: Stephen Chung
   • Reason: This paper introduces Unbiased Weight Maximization, providing a novel and theoretically grounded learning rule that can potentially speed up training in neural networks that emulate stochastic reinforcement learning agents.