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
Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test
Kathy Jang, Nathan Lichtlé, Eugene Vinitsky, Adit Shah, Matthew Bunting, Matthew Nice, Benedetto Piccoli, Benjamin Seibold, Daniel B. Work, Maria Laura Delle Monache, Jonathan Sprinkle, Jonathan W. Lee, Alexandre M. Bayen
Model-based deep reinforcement learning for accelerated learning from flow simulations
Andre Weiner, Janis Geise
SHM-Traffic: DRL and Transfer learning based UAV Control for Structural Health Monitoring of Bridges with Traffic
Divija Swetha Gadiraju, Saeed Eftekhar Azam, Deepak Khazanchi
Transformable Gaussian Reward Function for Socially-Aware Navigation with Deep Reinforcement Learning
Jinyeob Kim, Sumin Kang, Sungwoo Yang, Beomjoon Kim, Jargalbaatar Yura, Donghan Kim
Single-Reset Divide & Conquer Imitation Learning
Alexandre Chenu, Olivier Serris, Olivier Sigaud, Nicolas Perrin-Gilbert
Learning Interpretable Policies in Hindsight-Observable POMDPs through Partially Supervised Reinforcement Learning
Michael Lanier, Ying Xu, Nathan Jacobs, Chongjie Zhang, Yevgeniy Vorobeychik
FGeo-DRL: Deductive Reasoning for Geometric Problems through Deep Reinforcement Learning
Jia Zou, Xiaokai Zhang, Yiming He, Na Zhu, Tuo Leng