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
DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
Haoran Ye, Jiarui Wang, Zhiguang Cao, Helan Liang, Yong Li
An AI Chatbot for Explaining Deep Reinforcement Learning Decisions of Service-oriented Systems
Andreas Metzger, Jone Bartel, Jan Laufer
Deep Reinforcement Learning for the Heat Transfer Control of Pulsating Impinging Jets
Sajad Salavatidezfouli, Giovanni Stabile, Gianluigi Rozza
Diagnosing and exploiting the computational demands of videos games for deep reinforcement learning
Lakshmi Narasimhan Govindarajan, Rex G Liu, Drew Linsley, Alekh Karkada Ashok, Max Reuter, Michael J Frank, Thomas Serre
Interpretable Decision Tree Search as a Markov Decision Process
Hector Kohler, Riad Akrour, Philippe Preux
Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning
Haochen Zhang, Xi Chen, Lin F. Yang
Deep Reinforcement Learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advice
Johannes V. S. Busch, Robert Voelckner, Peter Sossalla, Christian L. Vielhaus, Roberto Calandra, Frank H. P. Fitzek
Two-Stage Learning of Highly Dynamic Motions with Rigid and Articulated Soft Quadrupeds
Francecso Vezzi, Jiatao Ding, Antonin Raffin, Jens Kober, Cosimo Della Santina