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
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection
Wanrong Yang, Alberto Acuto, Yihang Zhou, Dominik Wojtczak
Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous
Agni Bandyopadhyay, Guenther Waxenegger-Wilfing
Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability
Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters
Learning Bipedal Walking for Humanoid Robots in Challenging Environments with Obstacle Avoidance
Marwan Hamze (LISV), Mitsuharu Morisawa (AIST), Eiichi Yoshida (CNRS-AIST JRL)
Achieving Stable High-Speed Locomotion for Humanoid Robots with Deep Reinforcement Learning
Xinming Zhang, Xianghui Wang, Lerong Zhang, Guodong Guo, Xiaoyu Shen, Wei Zhang
Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain
Amin Zakaie Far, Mohammad Zakaie Far, Sonia Gharibzadeh, Shiva Zangeneh, Leila Amini, Morteza Rahimi
Multi-UAV Pursuit-Evasion with Online Planning in Unknown Environments by Deep Reinforcement Learning
Jiayu Chen, Chao Yu, Guosheng Li, Wenhao Tang, Xinyi Yang, Botian Xu, Huazhong Yang, Yu Wang
Safe Navigation for Robotic Digestive Endoscopy via Human Intervention-based Reinforcement Learning
Min Tan, Yushun Tao, Boyun Zheng, GaoSheng Xie, Lijuan Feng, Zeyang Xia, Jing Xiong
Improving Soft-Capture Phase Success in Space Debris Removal Missions: Leveraging Deep Reinforcement Learning and Tactile Feedback
Bahador Beigomi, Zheng H. Zhu
Optimizing Job Shop Scheduling in the Furniture Industry: A Reinforcement Learning Approach Considering Machine Setup, Batch Variability, and Intralogistics
Malte Schneevogt, Karsten Binninger, Noah Klarmann
Synthesizing Evolving Symbolic Representations for Autonomous Systems
Gabriele Sartor, Angelo Oddi, Riccardo Rasconi, Vieri Giuliano Santucci, Rosa Meo
Disentangling Uncertainty for Safe Social Navigation using Deep Reinforcement Learning
Daniel Flögel, Marcos Gómez Villafañe, Joshua Ransiek, Sören Hohmann
Audio-Driven Reinforcement Learning for Head-Orientation in Naturalistic Environments
Wessel Ledder, Yuzhen Qin, Kiki van der Heijden
SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning
Amogh Joshi, Adarsh Kumar Kosta, Kaushik Roy