1. 9.0 Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges
   • Authors: Ahmed Haj Yahmed, Altaf Allah Abbassi, Amin Nikanjam, Heng Li, Foutse Khomh
   • Reason: Exploring the implementation and deployment of DRL systems and highlighting challenges, which is critical for practical applications.
2. 8.6 An Intentional Forgetting-Driven Self-Healing Method For Deep Reinforcement Learning Systems
   • Authors: Ahmed Haj Yahmed, Rached Bouchoucha, Houssem Ben Braiek, Foutse Khomh
   • Reason: Proposing a novel approach to adapt DRL along with empirical results, potentially providing a new perspective for DRL research.
3. 8.3 Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotion
   • Authors: Yunho Kim, Hyunsik Oh, Jeonghyun Lee, Jinhyeok Choi, Gwanghyeon Ji, Moonkyu Jung, Donghoon Youm, Jemin Hwangbo
   • Reason: Introducing a novel RL framework for complex robotic systems and showing practical applications on legged robots, providing possible impact on robot locomotion.
4. 7.9 Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling
   • Authors: Yang Li, Fanjin Bu, Zhen Yang, Bin Wang, Meng Han
   • Reason: Proposing a practical scheduling model for integrated energy systems using multi-agent DRL that generates economic and environmental benefits.
5. 7.5 Conditional Kernel Imitation Learning for Continuous State Environments
   • Authors: Rishabh Agrawal, Nathan Dahlin, Rahul Jain, Ashutosh Nayyar
   • Reason: Introducing a new imitation learning framework for continuous state environments, addressing both theoretical and empirical study with comparison to state-of-the-art IL algorithms.