1. 9.4 Mastering the Game of Guandan with Deep Reinforcement Learning and Behavior Regulating
   • Authors: Yifan Yanggong, Hao Pan, Lei Wang
   • Reason: Proposes a novel framework for mastering a complex game using deep reinforcement learning, which is a significant achievement in the field and possibly advances AI gaming strategies.
2. 9.3 Reinforcement learning-assisted quantum architecture search for variational quantum algorithms
   • Authors: Akash Kundu
   • Reason: Addresses a critical challenge in the quantum computing domain with a reinforcement learning-based approach, which can potentially lead to significant advancements in quantum algorithm design.
3. 9.2 Enhancing Reinforcement Learning Agents with Local Guides
   • Authors: Paul Daoudi, Bogdan Robu, Christophe Prieur, Ludovic Dos Santos, Merwan Barlier
   • Reason: Introduces a novel algorithm that integrates local guide policies into reinforcement learning agents, which could influence the development of safe and effective RL solutions in safety-critical systems.
4. 9.0 AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning
   • Authors: Vasudev Gohil, Satwik Patnaik, Dileep Kalathil, Jeyavijayan Rajendran
   • Reason: Proposes an original reinforcement learning-based adversarial framework for evaluating the robustness of GNNs in hardware security, an area of increasing importance as GNNs are more widely adopted.
5. 8.9 Improving a Proportional Integral Controller with Reinforcement Learning on a Throttle Valve Benchmark
   • Authors: Paul Daoudi, Bojan Mavkov, Bogdan Robu, Christophe Prieur, Emmanuel Witrant, Merwan Barlier, Ludovic Dos Santos
   • Reason: Applies reinforcement learning to enhance traditional control systems, demonstrating RL’s potential in real-world, non-linear system optimization applications.