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
Evolutionary Strategy Guided Reinforcement Learning via MultiBuffer Communication
Adam Callaghan, Karl Mason, Patrick Mannion
Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning
Anna Penzkofer, Simon Schaefer, Florian Strohm, Mihai Bâce, Stefan Leutenegger, Andreas Bulling
Comprehensive Training and Evaluation on Deep Reinforcement Learning for Automated Driving in Various Simulated Driving Maneuvers
Yongqi Dong, Tobias Datema, Vincent Wassenaar, Joris van de Weg, Cahit Tolga Kopar, Harim Suleman
Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning
Henan Yuan, Penghui Li, Bart van Arem, Liujiang Kang, Yongqi Dong
Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization
Matias Alvo, Daniel Russo, Yash Kanoria
Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation
Jumman Hossain
Sim-to-real transfer of active suspension control using deep reinforcement learning
Viktor Wiberg, Erik Wallin, Arvid Fälldin, Tobias Semberg, Morgan Rossander, Eddie Wadbro, Martin Servin
Deep Reinforcement Learning for ESG financial portfolio management
Eduardo C. Garrido-Merchán, Sol Mora-Figueroa-Cruz-Guzmán, María Coronado-Vaca
Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management
Marc Velay, Bich-Liên Doan, Arpad Rimmel, Fabrice Popineau, Fabrice Daniel
PTDRL: Parameter Tuning using Deep Reinforcement Learning
Elias Goldsztejn, Tal Feiner, Ronen Brafman
Adaptive Ordered Information Extraction with Deep Reinforcement Learning
Wenhao Huang, Jiaqing Liang, Zhixu Li, Yanghua Xiao, Chuanjun Ji
Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork
Yonggang Jin, Chenxu Wang, Tianyu Zheng, Liuyu Xiang, Yaodong Yang, Junge Zhang, Jie Fu, Zhaofeng He
QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust Control
Zhehui Huang, Sumeet Batra, Tao Chen, Rahul Krupani, Tushar Kumar, Artem Molchanov, Aleksei Petrenko, James A. Preiss, Zhaojing Yang, Gaurav S. Sukhatme
Attention-based Open RAN Slice Management using Deep Reinforcement Learning
Fatemeh Lotfi, Fatemeh Afghah, Jonathan Ashdown
Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving
Jayabrata Chowdhury, Vishruth Veerendranath, Suresh Sundaram, Narasimhan Sundararajan
Evolutionary Curriculum Training for DRL-Based Navigation Systems
Max Asselmeier, Zhaoyi Li, Kelin Yu, Danfei Xu