Robustness remains a paramount concern in deep reinforcement learning (DRL), with randomized smoothing emerging as a key technique for enhancing this attribute. However, a notable gap exists in the performance of current smoothed DRL agents, often characterized by significantly low clean rewards and weak robustness. In response to this challenge, our study introduces innovative algorithms aimed at training effective smoothed robust DRL agents. We propose S-DQN and S-PPO, novel approaches that demonstrate remarkable improvements in clean rewards, empirical robustness, and robustness guarantee across standard RL benchmarks. Notably, our S-DQN and S-PPO agents not only significantly outperform existing smoothed agents by an average factor of $2.16\times$ under the strongest attack, but also surpass previous robustly-trained agents by an average factor of $2.13\times$. This represents a significant leap forward in the field. Furthermore, we introduce Smoothed Attack, which is $1.89\times$ more effective in decreasing the rewards of smoothed agents than existing adversarial attacks.
Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game
Guangxi University Key Laboratory of Intelligent Networking and Scenario System (School of Information and Communication, Guilin University of Electronic Technology)（广西智能网络与场景系统重点实验室（信息与通信学院，桂林电子科技大学））；National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology (Guangxi)（综合交通运输大数据应用技术国家工程实验室（广西））；School of Automation, Guangdong University of Technology（自动化学院，广东工业大学）；Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning（广西多媒体通信与网络技术重点实验室，南宁）；School of Computer and Electronic Information, Guangxi University（计算机与电子信息学院，广西大学）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
SAMP-HDRL: Segmented Allocation with Momentum-Adjusted Utility for Multi-agent Portfolio Management via Hierarchical Deep Reinforcement Learning
School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University（人工智能与先进计算学院，XJTLU创业学院（太仓），西安交通大学利物浦大学）；Department of Management Science and Engineering, School of Economics and Management, Xinjiang University（管理科学与工程系，经济管理学院，新疆大学）
Boosting Transferable Adversarial Attacks against Deep Reinforcement Learning
Nanyang Technological University（南洋理工大学）；University of California, Riverside（加利福尼亚大学河滨分校）；University of Connecticut（康涅狄格大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks
University of Electronic Science and Technology of China（电子科技大学）；Simula Metropolitan Center for Digital Engineering（Simula都市数字工程中心）；University of Oslo（奥斯陆大学）
BehaviorGuard: Online Backdoor Defense for Deep Reinforcement Learning
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络安全学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Department of Electronic and Electrical Engineering, University College London（伦敦大学学院电子与电气工程系）
Lyapunov Stability-Aware Stackelberg Game for Low-Altitude Economy: A Control-Oriented Pruning-Based DRL Approach
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系）；Department of Electrical and Computer Engineering, Sungkyunkwan University（Sungkyunkwan 大学电子与计算机工程系）；School of Electrical and Computer Engineering, University of Sydney（悉尼大学电气与计算机工程学院）；Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究生院）
Emergency Caching: Coded Caching-based Reliable Map Transmission in Emergency Networks
School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）；School of Electronic Engineering, Beijing University of Posts and Telecommunications（北京邮电大学电子工程学院）；Frontier Research Center, Peng Cheng Laboratory（鹏城实验室前沿研究中心）
Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management
organization= Department of Civil；Environmental Engineering , addressline= The Pennsylvania State University , city= University Park , postcode= 16802 , state= PA , country= USA；organization= Faculty of Architecture \& the Built Environment , addressline= Delft University of Technology , postcode= 2628 BL , state= Delft , country= The Netherlands
