1. 9.4 Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning
   • Authors: Md Masudur Rahman, Yexiang Xue
   • Reason: Focuses on the improvement of generalization for reinforcement learning agents by addressing the issue of overfitting to confounding features. Proposes a practical deep reinforcement learning algorithm which has shown improved performance in experiments.
2. 9.2 Measurement Tampering Detection Benchmark
   • Authors: Fabien Roger, Ryan Greenblatt, Max Nadeau, Buck Shlegeris, Nate Thomas
   • Reason: Discusses the concept of measuring tampering in AI systems and proposes new techniques to evaluate tampering detection. However, the approach does not reach maximum performance, indicating room for improvement.
3. 9.1 InstaTune: Instantaneous Neural Architecture Search During Fine-Tuning
   • Authors: Sharath Nittur Sridhar, Souvik Kundu, Sairam Sundaresan, Maciej Szankin, Anthony Sarah
   • Reason: Important for resource optimization. Proposes a method that leverages pre-trained weights for large models and generates a super-network during the fine-tuning stage.
4. 8.9 Deep Reinforcement Learning Based Framework for Mobile Energy Disseminator Dispatching to Charge On-the-Road Electric Vehicles
   • Authors: Jiaming Wang, Jiqian Dong, Sikai Chen, Shreyas Sundaram, Samuel Labi
   • Reason: Focuses on practical implications concerning electric vehicles and wireless charging, proposing a dispatching strategy for Mobile Energy Disseminators.
5. 8.8 Cyclophobic Reinforcement Learning
   • Authors: Stefan Sylvius Wagner, Peter Arndt, Jan Robine, Stefan Harmeling
   • Reason: Proposes a new intrinsic reward designed to encourage systematic exploration of the state space.