1. 9.4 RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels
   • Authors: Alexander Shmakov, Avisek Naug, Vineet Gundecha, Sahand Ghorbanpour, Ricardo Luna Gutierrez, Ashwin Ramesh Babu, Antonio Guillen, Soumyendu Sarkar
   • Reason: This paper presents a breakthrough in Deep Kernel Learning, combining it with transformer models and reinforcement learning to improve performance in high-dimensional optimization problems. The authority of the authors and the innovation in their approach attest to the potential influence of this paper.
2. 9.2 Small batch deep reinforcement learning
   • Authors: Johan Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro
   • Reason: The work presents an innovative study that contradicts the common practice of employing larger batch sizes in neural network training. Their empirical study proposing the reduction of batch size in deep reinforcement learning could influence the research direction in the field. This is published at NeurIPS, further verifying its influence.
3. 8.9 AdaRec: Adaptive Sequential Recommendation for Reinforcing Long-term User Engagement
   • Authors: Zhenghai Xue, Qingpeng Cai, Tianyou Zuo, Bin Yang, Lantao Hu, Peng Jiang, Kun Gai, Bo An
   • Reason: This paper presents a novel paradigm, AdaRec addressing challenges in reinforcement learning algorithms for recommendation systems to improve long-term user engagement. The extensive empirical analyses in both simulator-based and live sequential recommendation tasks make this paper potentially influential.
4. 8.7 PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability
   • Authors: Avisek Naug, Antonio Guillen, Ricardo Luna Gutiérrez, Vineet Gundecha, Dejan Markovikj, Lekhapriya Dheeraj Kashyap, Lorenz Krause, Sahand Ghorbanpour, Sajad Mousavi, Ashwin Ramesh Babu, Soumyendu Sarkar
   • Reason: The paper introduced PyDCM, a Python based Data Center Model that leverages reinforcement learning to optimize data center cooling and reduce power consumption. Given the global emphasis on sustainability, this paper offers significant value and potential influence in the field of tech and sustainability.
5. 8.5 Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study
   • Authors: Fouzi Boukhalfa, Reda Alami, Mastane Achab, Eric Moulines, Mehdi Bennis
   • Reason: In this paper, the authors address the technological debate of the automobile industry through the use of reinforcement learning for coordination of multiple V2X technologies. This paper has potential influence primarily in the automotive and telecommunications industries.