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
Proximal Curriculum with Task Correlations for Deep Reinforcement Learning
Georgios Tzannetos, Parameswaran Kamalaruban, Adish Singla
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning
Dhruva Tirumala, Markus Wulfmeier, Ben Moran, Sandy Huang, Jan Humplik, Guy Lever, Tuomas Haarnoja, Leonard Hasenclever, Arunkumar Byravan, Nathan Batchelor, Neil Sreendra, Kushal Patel, Marlon Gwira, Francesco Nori, Martin Riedmiller, Nicolas Heess
Behavior Imitation for Manipulator Control and Grasping with Deep Reinforcement Learning
Liu Qiyuan
Tabular and Deep Reinforcement Learning for Gittins Index
Harshit Dhankar, Kshitij Mishra, Tejas Bodas
Leveraging Procedural Generation for Learning Autonomous Peg-in-Hole Assembly in Space
Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez, Carol Martinez
Portfolio Management using Deep Reinforcement Learning
Ashish Anil Pawar, Vishnureddy Prashant Muskawar, Ritesh Tiku
HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach
Malte Lehna, Clara Holzhüter, Sven Tomforde, Christoph Scholz
Learning High-Quality Navigation and Zooming on Omnidirectional Images in Virtual Reality
Zidong Cao, Zhan Wang, Yexin Liu, Yan-Pei Cao, Ying Shan, Wei Zeng, Lin Wang
Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning
Hao Liu, Yi Shen, Wenjing Zhou, Yuelin Zou, Chang Zhou, Shuyao He
An Explainable Deep Reinforcement Learning Model for Warfarin Maintenance Dosing Using Policy Distillation and Action Forging
Sadjad Anzabi Zadeh, W. Nick Street, Barrett W. Thomas
Deep Reinforcement Learning for Bipedal Locomotion: A Brief Survey
Lingfan Bao, Joseph Humphreys, Tianhu Peng, Chengxu Zhou
Evaluating Collaborative Autonomy in Opposed Environments using Maritime Capture-the-Flag Competitions
Jordan Beason, Michael Novitzky, John Kliem, Tyler Errico, Zachary Serlin, Kevin Becker, Tyler Paine, Michael Benjamin, Prithviraj Dasgupta, Peter Crowley, Charles O'Donnell, John James
RUMOR: Reinforcement learning for Understanding a Model of the Real World for Navigation in Dynamic Environments
Diego Martinez-Baselga, Luis Riazuelo, Luis Montano
Decentralized Coordination of Distributed Energy Resources through Local Energy Markets and Deep Reinforcement Learning
Daniel May, Matthew Taylor, Petr Musilek
Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation
Qiang He, Tianyi Zhou, Meng Fang, Setareh Maghsudi