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.
1498papers
Papers
March 31, 2025
Nuclear Microreactor Control with Deep Reinforcement Learning
Leo Tunkle, Kamal Abdulraheem, Linyu Lin, Majdi I. RadaidehUniversity of Michigan●Idaho National LaboratoryMAER-Nav: Bidirectional Motion Learning Through Mirror-Augmented Experience Replay for Robot Navigation
Shanze Wang, Mingao Tan, Zhibo Yang, Biao Huang, Xiaoyu Shen, Hailong Huang, Wei ZhangHong Kong Polytechnic University●Eastern Institute of Technology●National University of Singapore●Harbin Institute of Technology
March 26, 2025
Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attacks
Zongyuan Zhang, Tianyang Duan, Zheng Lin, Dong Huang, Zihan Fang, Zekai Sun, Ling Xiong, Hongbin Liang, Heming Cui, Yong Cui, Yue GaoThe University of Hong Kong●Fudan University●Xihua University●Southwest Jiaotong University●Tsinghua UniversityState-Aware Perturbation Optimization for Robust Deep Reinforcement Learning
Zongyuan Zhang, Tianyang Duan, Zheng Lin, Dong Huang, Zihan Fang, Zekai Sun, Ling Xiong, Hongbin Liang, Heming Cui, Yong CuiThe University of Hong Kong●City University of Hong Kong●Xihua University●Southwest Jiaotong University●Tsinghua UniversityMulti-agent Uncertainty-Aware Pessimistic Model-Based Reinforcement Learning for Connected Autonomous Vehicles
Ruoqi Wen, Rongpeng Li, Xing Xu, Zhifeng ZhaoZhejiang University●Ltd●Zhejiang Lab
March 24, 2025
Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer Learning
Gautham Udayakumar Bekal, Ahmed Ghareeb, Ashish PujariEnlyte●University of Kirkuk●University of North Carolina at CharlotteAdventurer: Exploration with BiGAN for Deep Reinforcement Learning
Yongshuai Liu, Xin LiuDavis
March 21, 2025
Optimizing 2D+1 Packing in Constrained Environments Using Deep Reinforcement Learning
Victor Ulisses Pugliese, Oséias F. de A. Ferreira, Fabio A. FariaLeveraging Language Models for Out-of-Distribution Recovery in Reinforcement Learning
Chan Kim, Seung-Woo Seo, Seong-Woo KimSeoul National UniversityA New Segment Routing method with Swap Node Selection Strategy Based on Deep Reinforcement Learning for Software Defined Network
Miao Ye, Jihao Zheng, Qiuxiang Jiang, Yuan Huang, Ziheng Wang, Yong WangGuilin University of Electronic Technology●Guangxi Engineering Technology Research Center of Cloud Security and Cloud Service
March 17, 2025
Timing the Match: A Deep Reinforcement Learning Approach for Ride-Hailing and Ride-Pooling Services
Yiman Bao, Jie Gao, Jinke He, Frans A. Oliehoek, Oded CatsDelft University of TechnologySafeSlice: Enabling SLA-Compliant O-RAN Slicing via Safe Deep Reinforcement Learning
Ahmad M. Nagib, Hatem Abou-Zeid, Hossam S. HassaneinQueen’s University●University of Calgary
March 14, 2025
Low-cost Real-world Implementation of the Swing-up Pendulum for Deep Reinforcement Learning Experiments
Peter Böhm, Pauline Pounds, Archie C. ChapmanThe University of QueenslandTraining Directional Locomotion for Quadrupedal Low-Cost Robotic Systems via Deep Reinforcement Learning
Peter Böhm, Archie C. Chapman, Pauline PoundsThe University of Queensland