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
Multi-Robot Motion Planning: A Learning-Based Artificial Potential Field Solution
Dengyu Zhang, Guobin Zhu, Qingrui Zhang
Dynamic Interval Restrictions on Action Spaces in Deep Reinforcement Learning for Obstacle Avoidance
Tim Grams
DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback
Junfan Lin, Yuying Zhu, Lingbo Liu, Yang Liu, Guanbin Li, Liang Lin
Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles
Yining Ma, Wei Jiang, Lingtong Zhang, Junyi Chen, Hong Wang, Chen Lv, Xuesong Wang, Lu Xiong
High-speed Autonomous Racing using Trajectory-aided Deep Reinforcement Learning
Benjamin David Evans, Herman Arnold Engelbrecht, Hendrik Willem Jordaan
Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications
Jiangwei Wang, Shuo Yang, Ziyan An, Songyang Han, Zhili Zhang, Rahul Mangharam, Meiyi Ma, Fei Miao
Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learning
Tao Li, Haozhe Lei, Hao Guo, Mingsheng Yin, Yaqi Hu, Quanyan Zhu, Sundeep Rangan
On the Importance of Exploration for Generalization in Reinforcement Learning
Yiding Jiang, J. Zico Kolter, Roberta Raileanu
A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments
Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
A Transferability Metric Using Scene Similarity and Local Map Observation for DRL Navigation
Shiwei Lian, Feitian Zhang
Learned spatial data partitioning
Keizo Hori, Yuya Sasaki, Daichi Amagata, Yuki Murosaki, Makoto Onizuka
Reinforcement Learning-Based Control of CrazyFlie 2.X Quadrotor
Arshad Javeed, Valentín López Jiménez
RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control
Jonas Eschmann, Dario Albani, Giuseppe Loianno
A Grasp Pose is All You Need: Learning Multi-fingered Grasping with Deep Reinforcement Learning from Vision and Touch
Federico Ceola, Elisa Maiettini, Lorenzo Rosasco, Lorenzo Natale