Deep Reinforcement Learning
Deep reinforcement learning (DRL) aims to train agents to make optimal decisions in complex environments by learning through trial and error. Current research focuses on improving DRL's robustness, sample efficiency, and interpretability, often employing architectures like Proximal Policy Optimization (PPO), deep Q-networks (DQNs), and graph neural networks (GNNs) to address challenges in diverse applications such as robotics, game playing, and resource management. The resulting advancements have significant implications for various fields, enabling the development of more adaptable and efficient autonomous systems across numerous domains.
Papers
Small batch deep reinforcement learning
Johan Obando-Ceron, Marc G. Bellemare, Pablo Samuel Castro
How the level sampling process impacts zero-shot generalisation in deep reinforcement learning
Samuel Garcin, James Doran, Shangmin Guo, Christopher G. Lucas, Stefano V. Albrecht
A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling
Amjad Yousef Majid, Eduard Marin
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
Maziyar Khadivi, Todd Charter, Marjan Yaghoubi, Masoud Jalayer, Maryam Ahang, Ardeshir Shojaeinasab, Homayoun Najjaran
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
Matthew Thomas Jackson, Minqi Jiang, Jack Parker-Holder, Risto Vuorio, Chris Lu, Gregory Farquhar, Shimon Whiteson, Jakob Nicolaus Foerster
Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study
Fouzi Boukhalfa, Reda Alami, Mastane Achab, Eric Moulines, Mehdi Bennis
Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
Shengyi Huang, Jiayi Weng, Rujikorn Charakorn, Min Lin, Zhongwen Xu, Santiago Ontañón
Reliability Quantification of Deep Reinforcement Learning-based Control
Hitoshi Yoshioka, Hirotada Hashimoto
TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework
Yang Zhao, Jiaxi Yang, Wenbo Wang, Helin Yang, Dusit Niyato