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
Edge Delayed Deep Deterministic Policy Gradient: efficient continuous control for edge scenarios
Alberto Sinigaglia, Niccolò Turcato, Ruggero Carli, Gian Antonio Susto
Vision-Based Deep Reinforcement Learning of UAV Autonomous Navigation Using Privileged Information
Junqiao Wang, Zhongliang Yu, Dong Zhou, Jiaqi Shi, Runran Deng
Bounded Exploration with World Model Uncertainty in Soft Actor-Critic Reinforcement Learning Algorithm
Ting Qiao, Henry Williams, David Valencia, Bruce MacDonald
DRL4AOI: A DRL Framework for Semantic-aware AOI Segmentation in Location-Based Services
Youfang Lin, Jinji Fu, Haomin Wen, Jiyuan Wang, Zhenjie Wei, Yuting Qiang, Xiaowei Mao, Lixia Wu, Haoyuan Hu, Yuxuan Liang, Huaiyu Wan
Putting the Iterative Training of Decision Trees to the Test on a Real-World Robotic Task
Raphael C. Engelhardt, Marcel J. Meinen, Moritz Lange, Laurenz Wiskott, Wolfgang Konen
Action Mapping for Reinforcement Learning in Continuous Environments with Constraints
Mirco Theile, Lukas Dirnberger, Raphael Trumpp, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli
GRAM: Generalization in Deep RL with a Robust Adaptation Module
James Queeney, Xiaoyi Cai, Mouhacine Benosman, Jonathan P. How
Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach
Xiaowen Ye, Yuyi Mao, Xianghao Yu, Shu Sun, Liqun Fu, Jie Xu
Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning
John Subosits, Jenna Lee, Shawn Manuel, Paul Tylkin, Avinash Balachandran
Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning
Alejandro Mendoza Barrionuevo, Samuel Yanes Luis, Daniel Gutiérrez Reina, Sergio L. Toral Marín
Conformal Symplectic Optimization for Stable Reinforcement Learning
Yao Lyu, Xiangteng Zhang, Shengbo Eben Li, Jingliang Duan, Letian Tao, Qing Xu, Lei He, Keqiang Li
Step-by-Step Guidance to Differential Anemia Diagnosis with Real-World Data and Deep Reinforcement Learning
Lillian Muyama, Estelle Lu, Geoffrey Cheminet, Jacques Pouchot, Bastien Rance, Anne-Isabelle Tropeano, Antoine Neuraz, Adrien Coulet
GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach
Kou Tian, Chentao Yue, Changyang She, Yonghui Li, Branka Vucetic
RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networks
Xu Yang, Chenhui Lin, Haotian Liu, Wenchuan Wu
A Memory-Based Reinforcement Learning Approach to Integrated Sensing and Communication
Homa Nikbakht, Michèle Wigger, Shlomo Shamai (Shitz), H. Vincent Poor