1. 9.2 Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
   • Authors: Zida Wu, Mathieu Lauriere, Samuel Jia Cong Chua, Matthieu Geist, Olivier Pietquin, Ankur Mehta
   • Reason: The paper proposes a novel deep reinforcement learning algorithm for Mean-Field Games, a domain that addresses complex multi-agent interactions. The presence of well-known authors in the field and its potential impact on understanding large-scale multi-agent systems gives it a high score.
2. 9.1 Learning Adversarial MDPs with Stochastic Hard Constraints
   • Authors: Francesco Emanuele Stradi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
   • Reason: This work addresses constrained Markov decision processes with adversarial losses, a challenging area in RL, with implications for a range of real-world applications. The combination of adversarial learning and CMDPs likely presents significant advancements in the field.
3. 8.9 Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
   • Authors: Jesse Farebrother, Jordi Orbay, Quan Vuong, Adrien Ali Taïga, Yevgen Chebotar, Ted Xiao, Alex Irpan, Sergey Levine, Pablo Samuel Castro, Aleksandra Faust, Aviral Kumar, Rishabh Agarwal
   • Reason: Given the participation of some highly recognized contributors such as Sergey Levine, this paper suggests a paradigm shift in training value functions. The potential for scalability and applicability in large-network RL tasks could significantly influence the field.
4. 8.8 Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning
   • Authors: Zifan Xu, Amir Hossain Raj, Xuesu Xiao, Peter Stone
   • Reason: Peter Stone is a prominent figure in robotics, and the paper presents advancements in locomotion through confined 3D spaces. The applications for robotics are clear and potentially transformative, warranting a high influence score.
5. 8.5 Reinforcement Learning Jazz Improvisation: When Music Meets Game Theory
   • Authors: Vedant Tapiavala, Joshua Piesner, Sourjyamoy Barman, Feng Fu
   • Reason: Although a niche application, the innovative crossover between RL and jazz improvisation presents a unique and potentially influential exploration. This paper could open new avenues for the application of reinforcement learning in arts and entertainment.