1 citations · 1 across the 6 of their papers we have counts for
6 papers
Constraint-Enhanced Reinforcement Learning Based on Dynamic Decoupled Spherical Radial Squashing
Qijun Liao, Zhaoxin Yu, Jue Yang
When deploying reinforcement learning policies to physical robots, actuator rate constraints -- hard limits on how fast each joint can move per control step -- are unavoidable. The…
Hybrid Energy-Aware Reward Shaping: A Unified Lightweight Physics-Guided Methodology for Policy Optimization
Qijun Liao, Jue Yang, Yiting Kang +3
Deep reinforcement learning for continuous control often suffers from high variance, low energy efficiency, and poor generalization under distribution shift, as purely data-driven…
Compositional Learning for Modular Multi-Agent Self-Organizing Networks
Qi Liao, Parijat Bhattacharjee
Self-organizing networks face challenges from complex parameter interdependencies and conflicting objectives. This study introduces two compositional learning approaches-Compositio…
Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods
Tianlun Hu, Qi Liao, Qiang Liu +2
Network slicing is a key technique in 5G and beyond for efficiently supporting diverse services. Many network slicing solutions rely on deep learning to manage complex and high-dim…
AdaSem: Adaptive Goal-Oriented Semantic Communications for End-to-End Camera Relocalization
Qi Liao, Tze-Yang Tung
Recently, deep autoencoders have gained traction as a powerful method for implementing goal-oriented semantic communications systems. The idea is to train a mapping from the source…
Task-oriented Communication Design in Cyber-Physical Systems: A Survey on Theory and Applications
Arsham Mostaani, Thang X. Vu, Shree Krishna Sharma +3
Communications system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given chann…