1. 9.1 The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning
   • Authors: Anya Sims, Cong Lu, Yee Whye Teh
   • Reason: Authors with leading authority in the field, compelling evidence of a new foundational issue in model-based reinforcement learning, and a novel solution proposed. Likely to influence subsequent research.
2. 8.9 Skill or Luck? Return Decomposition via Advantage Functions
   • Authors: Hsiao-Ru Pan, Bernhard Schölkopf
   • Reason: The paper is authored by researchers with strong reputations, addresses a key problem of off-policy learning in RL, and provides an innovative decomposition method, attracting interest in the RL community.
3. 8.6 Offline Multi-task Transfer RL with Representational Penalization
   • Authors: Avinandan Bose, Simon Shaolei Du, Maryam Fazel
   • Reason: Strong author team and addresses an important issue in offline RL with a unique approach to representation transfer, making it potentially influential.
4. 8.2 Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies
   • Authors: Xiangyu Liu, Chenghao Deng, Yanchao Sun, Yongyuan Liang, Furong Huang
   • Reason: Notable contribution to robustness in RL with innovative adaptive defense mechanism, spotlight presentation at a major conference increases its potential impact.
5. 7.7 In deep reinforcement learning, a pruned network is a good network
   • Authors: Johan Obando-Ceron, Aaron Courville, Pablo Samuel Castro
   • Reason: The paper offers practical improvements to network efficiency in deep RL with potential for broad application, although the influence may be more incremental in nature.