1. 9.5 Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks
   • Authors: Marwa Chafii, Salmane Naoumi, Reda Alami, Ebtesam Almazrouei, Mehdi Bennis, Merouane Debbah
   • Reason: The work introduces a multi-agent reinforcement learning model addressing complex wireless network scenarios. This could have a significant impact on the future 6G networks development.
2. 9.3 The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems
   • Authors: Kai-Chieh Hsu, Haimin Hu, Jaime Fernández Fisac
   • The focus of the paper on the critical aspect of safety in autonomous systems positions it as a potentially influential work given the increasing prevalence of AI-based automation. The authors also propose an integrative approach to enhance both model-based and data-driven methods.
3. 9.2 Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learning
   • Authors: Xiao Liu, Wubing Chen, Mao Tan
   • Reason: The paper presents a novel method for Interpretable Policy Extraction in Reinforcement Learning. This could improve predictability and transparency of decision-making processes in RL.
4. 8.9 Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models
   • Authors: Sumeet Singh, Stephen Tu, Vikas Sindhwani
   • This paper’s emphasis on the practicality of energy-based models and its empirical validation by outperforming other state-of-the-art approaches makes it a noteworthy paper in the field of reinforcement learning.
5. 8.8 Risk-Aware Reinforcement Learning through Optimal Transport Theory
   • Authors: Ali Baheri
   • Reason: This work introduces a risk-aware framework for reinforcement learning. It pioneers the integration of Optimal Transport theory with RL to ensure reliable decision-making in uncertain environments.
6. 8.7 ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning
   • Authors: Chenxiao Gao, Chenyang Wu, Mingjun Cao, Rui Kong, Zongzhang Zhang, Yang Yu
   • This paper demonstrates a potential improvement in the Decision Transformer by conditioning it on estimated advantages. It further emphasizes the method’s effectiveness through extensive benchmarking.
7. 8.5 Adaptive User-centered Neuro-symbolic Learning for Multimodal Interaction with Autonomous Systems
   • Authors: Amr Gomaa, Michael Feld
   • This paper offers a novel perspective on multimodal interaction with autonomous systems, emphasizing human learning techniques. It’s proposals for advancing AI makes it a potential impactful work.
8. 8.4 Toward Discretization-Consistent Closure Schemes for Large Eddy Simulation Using Reinforcement Learning
   • Authors: Andrea Beck, Marius Kurz
   • Reason: Authors propose an innovative approach to the problem of discretization in ‘Large Eddy Simulation’ using Reinforcement Learning. This paper could stimulate research in this direction.
9. 8.1 Interpretable learning of effective dynamics for multiscale systems
   • Authors: Emmanuel Menier, Sebastian Kaltenbach, Mouadh Yagoubi, Marc Schoenauer, Petros Koumoutsakos
   • While playing an important role in advancing the understandability of AI models, this work’s focus on providing the added benefit of interpretability to state-of-the-art recurrent neural network-based approaches is what highlights it for potential influence.
10. 8.1 Verifiable Reinforcement Learning Systems via Compositionality
    • Authors: Cyrus Neary, Aryaman Singh Samyal, Christos Verginis, Murat Cubuktepe, Ufuk Topcu
    • Reason: The paper proposes a framework for verifiable and compositional reinforcement learning. The approach could be of significant interest to the community aiming towards more reliable RL models.