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 Based Framework for Mobile Energy Disseminator Dispatching to Charge On-the-Road Electric Vehicles
Jiaming Wang, Jiqian Dong, Sikai Chen, Shreyas Sundaram, Samuel Labi
Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics
Zexin Li, Aritra Samanta, Yufei Li, Andrea Soltoggio, Hyoseung Kim, Cong Liu
Optimal Economic Gas Turbine Dispatch with Deep Reinforcement Learning
Manuel Sage, Martin Staniszewski, Yaoyao Fiona Zhao
Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm
Colin Bellinger, Laurence Lamarche-Cliche
Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning
Binqi Sun, Mirco Theile, Ziyuan Qin, Daniele Bernardini, Debayan Roy, Andrea Bastoni, Marco Caccamo
Deep Reinforcement Learning for Uplink Scheduling in NOMA-URLLC Networks
Benoît-Marie Robaglia, Marceau Coupechoux, Dimitrios Tsilimantos
Target-independent XLA optimization using Reinforcement Learning
Milan Ganai, Haichen Li, Theodore Enns, Yida Wang, Randy Huang
Pretty darn good control: when are approximate solutions better than approximate models
Felipe Montealegre-Mora, Marcus Lapeyrolerie, Melissa Chapman, Abigail G. Keller, Carl Boettiger
Learn With Imagination: Safe Set Guided State-wise Constrained Policy Optimization
Feihan Li, Yifan Sun, Weiye Zhao, Rui Chen, Tianhao Wei, Changliu Liu
An Intentional Forgetting-Driven Self-Healing Method For Deep Reinforcement Learning Systems
Ahmed Haj Yahmed, Rached Bouchoucha, Houssem Ben Braiek, Foutse Khomh
Deploying Deep Reinforcement Learning Systems: A Taxonomy of Challenges
Ahmed Haj Yahmed, Altaf Allah Abbassi, Amin Nikanjam, Heng Li, Foutse Khomh