1. 9.2 Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning
   • Authors: Parham Gohari, Matthew Hale, Ufuk Topcu
   • Reason: The paper addresses privacy concerns in Co-MARL which is a critical aspect for adoption in sensitive applications and is accepted at a prestigious IEEE conference.
2. 8.7 Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination
   • Authors: Lu Wen, Songan Zhang, H. Eric Tseng, Huei Peng
   • Reason: Introduces a context-based Meta RL algorithm promising data efficiency and generalization capabilities; this innovation is essential for rapid adaptation in RL.
3. 8.6 An advantage based policy transfer algorithm for reinforcement learning with metrics of transferability
   • Authors: Md Ferdous Alam, Parinaz Naghizadeh, David Hoelzle
   • Reason: Presents an off-policy transfer RL algorithm that could significantly increase the efficiency of RL applications, reducing computing resources and enhancing scalability.
4. 8.4 Learning Predictive Safety Filter via Decomposition of Robust Invariant Set
   • Authors: Zeyang Li, Chuxiong Hu, Weiye Zhao, Changliu Liu
   • Reason: Offers a novel framework combining RMPC and RL for ensuring safety in nonlinear systems, providing a balance between computational efficiency and safety guarantees.
5. 8.1 Towards Continual Reinforcement Learning for Quadruped Robots
   • Authors: Giovanni Minelli, Vassilis Vassiliades
   • Reason: Investigates continual learning in real-world quadruped robots, a step towards adaptive and resilient robotic systems that can learn post-deployment.