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 Agents for Strategic Production Policies in Microeconomic Market Simulations
Eduardo C. Garrido-Merchán, Maria Coronado-Vaca, Álvaro López-López, Carlos Martinez de Ibarreta
Efficient Diversity-based Experience Replay for Deep Reinforcement Learning
Kaiyan Zhao, Yiming Wang, Yuyang Chen, Xiaoguang Niu, Yan Li, Leong Hou U
Enhancing Battery Storage Energy Arbitrage with Deep Reinforcement Learning and Time-Series Forecasting
Manuel Sage, Joshua Campbell, Yaoyao Fiona Zhao
Adversarial Environment Design via Regret-Guided Diffusion Models
Hojun Chung, Junseo Lee, Minsoo Kim, Dohyeong Kim, Songhwai Oh
MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services
Hongjia Wu, Hui Zeng, Zehui Xiong, Jiawen Kang, Zhiping Cai, Tse-Tin Chan, Dusit Niyato, Zhu Han
An Enhanced Hierarchical Planning Framework for Multi-Robot Autonomous Exploration
Gengyuan Cai, Luosong Guo, Xiangmao Chang
RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space
Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Carlee Joe-Wong, Gina Adam, Nathaniel D. Bastian, Tian Lan
Long-distance Geomagnetic Navigation in GNSS-denied Environments with Deep Reinforcement Learning
Wenqi Bai, Xiaohui Zhang, Shiliang Zhang, Songnan Yang, Yushuai Li, Tingwen Huang
Offline reinforcement learning for job-shop scheduling problems
Imanol Echeverria, Maialen Murua, Roberto Santana
Patrol Security Game: Defending Against Adversary with Freedom in Attack Timing, Location, and Duration
Hao-Tsung Yang, Ting-Kai Weng, Ting-Yu Chang, Kin Sum Liu, Shan Lin, Jie Gao, Shih-Yu Tsai
Benchmarking Deep Reinforcement Learning for Navigation in Denied Sensor Environments
Mariusz Wisniewski, Paraskevas Chatzithanos, Weisi Guo, Antonios Tsourdos
Streaming Deep Reinforcement Learning Finally Works
Mohamed Elsayed, Gautham Vasan, A. Rupam Mahmood
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents
Nils Grandien, Quentin Delfosse, Kristian Kersting