1. 9.1 Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for the Model-free LQR
   • Authors: Leonardo F. Toso, Donglin Zhan, James Anderson, Han Wang
   • Reason: The paper tackles a fundamental and challenging problem in RL (learning LQR in a multi-task setting) and extends the influential MAML framework to this domain along with providing theoretical analyses, which may have wide-reaching impact within the RL community and beyond.
2. 9.0 CaRiNG: Learning Temporal Causal Representation under Non-Invertible Generation Process
   • Authors: Guangyi Chen, Yifan Shen, Zhenhao Chen, Xiangchen Song, Yuewen Sun, Weiran Yao, Xiao Liu, Kun Zhang
   • Reason: The paper introduces a novel identifiability theory in the context of causal representation learning, which is an emerging topic. The influence may stem from its potential applicability to a range of real-world problems and the establishment of theoretical grounds for solving them.
3. 8.9 Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks
   • Authors: Eura Nofshin, Siddharth Swaroop, Weiwei Pan, Susan Murphy, Finale Doshi-Velez
   • Reason: This paper’s novel approach to modeling and influencing human behavior through RL could have a significant impact on both AI and behavior science fields, especially due to the involvement of renowned researchers such as Finale Doshi-Velez and Susan Murphy.
4. 8.7 Off-Policy Primal-Dual Safe Reinforcement Learning
   • Authors: Zifan Wu, Bo Tang, Qian Lin, Chao Yu, Shangqin Mao, Qianlong Xie, Xingxing Wang, Dong Wang
   • Reason: The paper addresses the critical issue of safety constraints in off-policy RL methods, an area that is gaining increasing attention in sensitive applications such as autonomous driving.
5. 8.6 Fully Independent Communication in Multi-Agent Reinforcement Learning
   • Authors: Rafael Pina, Varuna De Silva, Corentin Artaud, Xiaolan Liu
   • Reason: This paper delves into communication strategies among independent agents, which is a practical and useful area of study in multi-agent systems. However, it might be of slightly lesser influence compared to other papers due to the narrower focus on communication aspects.