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
FLARE: Fingerprinting Deep Reinforcement Learning Agents using Universal Adversarial Masks
Buse G. A. Tekgul, N. Asokan
An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading
Shuyang Wang, Diego Klabjan
Evaluation of Safety Constraints in Autonomous Navigation with Deep Reinforcement Learning
Brian Angulo, Gregory Gorbov, Aleksandr Panov, Konstantin Yakovlev
A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatch
Shengren Hou, Edgar Mauricio Salazar Duque, Peter Palensky, Pedro P. Vergara
Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing
Max Yang, Yijiong Lin, Alex Church, John Lloyd, Dandan Zhang, David A. W. Barton, Nathan F. Lepora
Deep reinforcement learning for the dynamic vehicle dispatching problem: An event-based approach
Edyvalberty Alenquer Cordeiro, Anselmo Ramalho Pitombeira-Neto
Aeolus Ocean -- A simulation environment for the autonomous COLREG-compliant navigation of Unmanned Surface Vehicles using Deep Reinforcement Learning and Maritime Object Detection
Andrew Alexander Vekinis, Stavros Perantonis