1. 9.2 Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms
   • Authors: Marwan Dhuheir, Aiman Erbad, Ala Al-Fuqaha
   • Reason: The paper presents a novel optimization model using meta-reinforcement learning for UAV swarms in critical applications which is a significant and practical contribution to the field of RL applied to emergency response. The acceptance at a reputable conference, GlobeCom, and a clear applied impact on energy consumption and service coverage indicate a potentially influential approach.
2. 9.0 Detecting Hidden Triggers: Mapping Non-Markov Reward Functions to Markov
   • Authors: Gregory Hyde, Eugene Santos Jr
   • Reason: Proposes a new framework for dealing with non-Markov reward functions by learning Reward Machines, which is a substantial contribution to the RL theory. The practical implications for learning reward dependencies can influence a range of RL applications.
3. 8.8 Asynchronous Parallel Reinforcement Learning for Optimizing Propulsive Performance in Fin Ray Control
   • Authors: Xin-Yang Liu, Dariush Bodaghi, Qian Xue, Xudong Zheng, Jian-Xun Wang
   • Reason: Introduces a novel off-policy DRL algorithm with asynchronous parallel training, showing significant advancements in optimizing propulsive performance. The thorough validation gives it potential influence, especially with the promise of scalable parallelism in complex environments.
4. 8.6 Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot
   • Authors: Xinda Qi, Dong Chen, Zhaojian Li, Xiaobo Tan
   • Reason: The novel Back-stepping Experience Replay technique addresses key issues in learning efficiency and might directly impact reinforcement learning for robotics, especially in complex interaction scenarios, which is a promising and growing research area.
5. 8.5 Multi-Agent Generative Adversarial Interactive Self-Imitation Learning for AUV Formation Control and Obstacle Avoidance
   • Authors: Zheng Fang, Tianhao Chen, Dong Jiang, Zheng Zhang, Guangliang Li
   • Reason: The paper extends MAGAIL with the innovative MAGAISIL algorithm, potentially improving multi-agent learning beyond expert demonstrations. This approach is practical for multi-AUV control tasks which are crucial in underwater operations, possibly influencing a niche but important area within RL.