6 citations · 6 across the 8 of their papers we have counts for
9 papers
ReForge: Keeping ABR Algorithms Never Finished with Verified Large Language Model Edits
Zhiqiang He, Zhi Liu
Designing an ABR algorithm for one network scenario takes an engineer months, and large language models now do this work in hours, matching or beating hand-built designs. But eithe…
NSMA: Neuro-Symbolic Manifold Alignment for Generalizable Adaptive Bitrate Streaming under Texture Shift
Zhiqiang He, Zhi Liu
For decades, ABR has kept two kinds of intelligence apart. Neural policies learn rich behaviors yet forget them the moment the environment changes; rules never learn, and never for…
PRIME: Plasticity Recovery in Multi-Agent Environments for UAV-Assisted Emergency Communication Networks
Wen Qiu, Zhiqiang He, Wei Zhao +1
Most reinforcement learning controllers for these networks assume stationary conditions, and the few that handle change react to the external environment while leaving the network'…
Plasticity-Enhanced Multi-Agent Mixture of Experts for Dynamic Objective Adaptation in UAVs-Assisted Emergency Communication Networks
Wen Qiu, Zhiqiang He, Wei Zhao +1
Unmanned aerial vehicles serving as aerial base stations can rapidly restore connectivity after disasters, yet abrupt changes in user mobility and traffic demands shift the quality…
Rethinking Plasticity in Deep Reinforcement Learning
Zhiqiang He
This paper investigates the fundamental mechanisms driving plasticity loss in deep reinforcement learning (RL), a critical challenge where neural networks lose their ability to ada…
Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignment
Leizhen Wang, Peibo Duan, Cheng Lyu +4
The evolution of metropolitan cities and the increase in travel demands impose stringent requirements on traffic assignment methods. Multi-agent reinforcement learning (MARL) appro…