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
AutomaChef: A Physics-informed Demonstration-guided Learning Framework for Granular Material Manipulation
Minglun Wei, Xintong Yang, Yu-Kun Lai, Seyed Amir Tafrishi, Ze Ji
Deep Reinforcement Learning-based Quadcopter Controller: A Practical Approach and Experiments
Truong-Dong Do, Nguyen Xuan Mung, Sung Kyung Hong
Adaptive Swarm Mesh Refinement using Deep Reinforcement Learning with Local Rewards
Niklas Freymuth, Philipp Dahlinger, Tobias Würth, Simon Reisch, Luise Kärger, Gerhard Neumann
Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Control
Jonaid Shianifar, Michael Schukat, Karl Mason
Explore-Go: Leveraging Exploration for Generalisation in Deep Reinforcement Learning
Max Weltevrede, Felix Kaubek, Matthijs T.J. Spaan, Wendelin Böhmer
Hierarchical Reinforcement Learning for Swarm Confrontation with High Uncertainty
Qizhen Wu, Kexin Liu, Lei Chen, Jinhu Lü
Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges
Nan Cheng, Xiucheng Wang, Zan Li, Zhisheng Yin, Tom Luan, Xuemin Shen
Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling
Constantin Waubert de Puiseau, Christian Dörpelkus, Jannik Peters, Hasan Tercan, Tobias Meisen
Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning
Zhiyu Shao, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Jiangzhou Wang, Khaled B. Letaief
Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models
Som Sagar, Aditya Taparia, Ransalu Senanayake
DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach
Zhang Liu, Hongyang Du, Junzhe Lin, Zhibin Gao, Lianfen Huang, Seyyedali Hosseinalipour, Dusit Niyato