1. 9.2 Accelerating Exploration with Unlabeled Prior Data
   • Authors: Qiyang Li, Jason Zhang, Dibya Ghosh, Amy Zhang, Sergey Levine
   • Reason: The paper addresses the significant challenge of sparse reward signals in reinforcement learning with innovative and practical solutions, authored by experts including Sergey Levine, a highly influential figure in RL.
2. 8.9 Signal Temporal Logic-Guided Apprenticeship Learning
   • Authors: Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis
   • Reason: The paper tackles the complex issue of temporal dependencies in apprenticeship learning with a potential for wide impact on robotics and policy learning methods.
3. 8.6 Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System
   • Authors: Xiangguo Sun, Hong Cheng, Hang Dong, Bo Qiao, Si Qin, Qingwei Lin
   • Reason: This work proposes an original framework for improving scoring systems through “counter-empirical attacking,” making it relevant for platforms using such systems, including financial services.
4. 8.4 Anytime-Constrained Reinforcement Learning
   • Authors: Jeremy McMahan, Xiaojin Zhu
   • Reason: Anytime constraints are a new addition to constrained MDPs, which could be foundational, and the authors provide planning and learning algorithms that indicate the potential for practical applications.
5. 7.9 When Meta-Learning Meets Online and Continual Learning: A Survey
   • Authors: Jaehyeon Son, Soochan Lee, Gunhee Kim
   • Reason: Although a survey paper, it provides comprehensive coverage of significant learning paradigms that have potential cross-applications and combinations in reinforcement learning.