1. 9.5 MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment
   • Authors: Ziyan Wang, Yali Du, Yudi Zhang, Meng Fang, Biwei Huang
   • Reason: Authors offer a promising approach to credit assignment in offline MARL, a foundational aspect of efficient multi-agent systems, with solid theoretical backing, indicating a high potential for influence in developing robust multi-agent RL systems.
2. 9.3 SDSRA: A Skill-Driven Skill-Recombination Algorithm for Efficient Policy Learning
   • Authors: Eric H. Jiang, Andrew Lizarraga
   • Reason: Presentation of a novel RL algorithm that enhances efficiency, with reported superior performance over existing techniques. The innovative combination with skill-based strategies suggests a strong impact on multiple complex tasks within RL.
3. 8.9 Generalized Contrastive Divergence: Joint Training of Energy-Based Model and Diffusion Model through Inverse Reinforcement Learning
   • Authors: Sangwoong Yoon, Dohyun Kwon, Himchan Hwang, Yung-Kyun Noh, Frank C. Park
   • Reason: The paper introduces a novel objective function for simultaneous EBM and sampler training, presenting a unique perspective that aligns with inverse RL, with potential implications for enhancing sample quality and training efficiency.
4. 8.7 Diffused Task-Agnostic Milestone Planner
   • Authors: Mineui Hong, Minjae Kang, Songhwai Oh
   • Reason: The proposed method addresses long-term planning and multi-task decision-making using diffusion models, showing superior results in benchmarks which could significantly influence the field of long-horizon, sparse-reward tasks and planning.
5. 8.6 I-PHYRE: Interactive Physical Reasoning
   • Authors: Shiqian Li, Kewen Wu, Chi Zhang, Yixin Zhu
   • Reason: This work extends the realm of physical reasoning in RL to dynamic scenes and introduces a new framework, thereby highlighting a gap in current capabilities and the necessity of further research, indicating possible influence on future interactive RL systems.