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
Deep Reinforcement Learning for Time-Critical Wilderness Search And Rescue Using Drones
Jan-Hendrik Ewers, David Anderson, Douglas Thomson
GASE: Graph Attention Sampling with Edges Fusion for Solving Vehicle Routing Problems
Zhenwei Wang, Ruibin Bai, Fazlullah Khan, Ender Ozcan, Tiehua Zhang
Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers
Fan Shi, Chong Zhang, Takahiro Miki, Joonho Lee, Marco Hutter, Stelian Coros
Going into Orbit: Massively Parallelizing Episodic Reinforcement Learning
Jan Oberst, Johann Bonneau
Enhancing Vehicle Aerodynamics with Deep Reinforcement Learning in Voxelised Models
Jignesh Patel, Yannis Spyridis, Vasileios Argyriou
Deep Dive into Model-free Reinforcement Learning for Biological and Robotic Systems: Theory and Practice
Yusheng Jiao, Feng Ling, Sina Heydari, Nicolas Heess, Josh Merel, Eva Kanso
On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester
Optimizing Deep Reinforcement Learning for American Put Option Hedging
Reilly Pickard, F. Wredenhagen, Y. Lawryshyn
CIER: A Novel Experience Replay Approach with Causal Inference in Deep Reinforcement Learning
Jingwen Wang, Dehui Du, Yida Li, Yiyang Li, Yikang Chen
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments
Ke Liu, Fan Hu, Hui Lin, Xi Cheng, Jianan Chen, Jilin Song, Siyuan Feng, Gaofeng Su, Chen Zhu