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
Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learning
Mark Abdelshiheed, John Wesley Hostetter, Tiffany Barnes, Min Chi
How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning
Haodong Feng, Yue Wang, Hui Xiang, Zhiyang Jin, Dixia Fan
Robust Route Planning with Distributional Reinforcement Learning in a Stochastic Road Network Environment
Xi Lin, Paul Szenher, John D. Martin, Brendan Englot
Bridging RL Theory and Practice with the Effective Horizon
Cassidy Laidlaw, Stuart Russell, Anca Dragan
FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
Kyle Stachowicz, Dhruv Shah, Arjun Bhorkar, Ilya Kostrikov, Sergey Levine
Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach
Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama
Torque-based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfer
Donghyeon Kim, Glen Berseth, Mathew Schwartz, Jaeheung Park
Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach
Rishi Hazra, Luc De Raedt
Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving
Marvin Klimke, Benjamin Völz, Michael Buchholz
Leveraging Deep Reinforcement Learning for Metacognitive Interventions across Intelligent Tutoring Systems
Mark Abdelshiheed, John Wesley Hostetter, Tiffany Barnes, Min Chi
Control and Coordination of a SWARM of Unmanned Surface Vehicles using Deep Reinforcement Learning in ROS
Shrudhi R S, Sreyash Mohanty, Dr. Susan Elias
Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices
Quintin Fettes, Avinash Karanth, Razvan Bunescu, Brandon Beckwith, Sreenivas Subramoney