1. 9.3 Efficiently Escaping Saddle Points for Non-Convex Policy Optimization
   • Authors: Sadegh Khorasani, Saber Salehkaleybar, Negar Kiyavash, Niao He, Matthias Grossglauser
   • Reason: Presents a novel variance-reduced second-order method with significant theoretical contributions and improvements on sample complexity for convergence, backed by experimental results that outperform the state-of-the-art. High-authority authors with strong affiliations contribute to potential wide influence.
2. 9.2 Supported Trust Region Optimization for Offline Reinforcement Learning
   • Authors: Yixiu Mao, Hongchang Zhang, Chen Chen, Yi Xu, Xiangyang Ji
   • Reason: Addresses critical issues in offline reinforcement learning and proposes a conceptually new optimization technique with theoretical guarantees. Accepted at ICML 2023, indicative of the paper’s influence in the RL community.
3. 9.0 On the Foundation of Distributionally Robust Reinforcement Learning
   • Authors: Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou
   • Reason: Provides a comprehensive modeling framework for DRRL which deepens the theoretical understanding of the field. Collaborative effort by authors with strong research credentials. Additionally, rigorous exploration of various controller and adversary attributes.
4. 8.8 Purpose in the Machine: Do Traffic Simulators Produce Distributionally Equivalent Outcomes for Reinforcement Learning Applications?
   • Authors: Rex Chen, Kathleen M. Carley, Fei Fang, Norman Sadeh
   • Reason: Investigates critical real-world deployability issues for RL agents trained on simulators, with significant implications for intelligent transportation systems. The authority of the authors and the practical relevance of the topic promise it substantial influence.
5. 8.6 Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
   • Authors: Wei Wen, Kuang-Hung Liu, Igor Fedorov, et al.
   • Reason: Describes a comprehensive real-world application of NAS methods at Meta scale, tested against production models. The extensive author list includes researchers from recognized institutions, which supports potential industry-wide impact.