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
Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning
Yang Li, Wenjie Ma, Yuanzheng Li, Sen Li, Zhe Chen, Mohammad Shahidehpor
Goal-conditioned Offline Planning from Curious Exploration
Marco Bagatella, Georg Martius
Optimization Theory Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systems
Hamida Qumber Ali, Amirhassan Babazadeh Darabi, Sinem Coleri
Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing
Zhengming Zhang, Yongming Huang, Cheng Zhang, Qingbi Zheng, Luxi Yang, Xiaohu You
Agent-Aware Training for Agent-Agnostic Action Advising in Deep Reinforcement Learning
Yaoquan Wei, Shunyu Liu, Jie Song, Tongya Zheng, Kaixuan Chen, Yong Wang, Mingli Song
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy
Jung-Hoon Cho, Sirui Li, Jeongyun Kim, Cathy Wu
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Jiachen Li, David Isele, Kanghoon Lee, Jinkyoo Park, Kikuo Fujimura, Mykel J. Kochenderfer
Program Machine Policy: Addressing Long-Horizon Tasks by Integrating Program Synthesis and State Machines
Yu-An Lin, Chen-Tao Lee, Guan-Ting Liu, Pu-Jen Cheng, Shao-Hua Sun
Utilizing Explainability Techniques for Reinforcement Learning Model Assurance
Alexander Tapley, Kyle Gatesman, Luis Robaina, Brett Bissey, Joseph Weissman