6 papers
Learning an Opponent-aware Anti-jamming Strategy via Online Convex Optimization
Liangqi Liu, Wenqiang Pu, Yingru Li +1
The dynamic competition against intelligent jammer systems presents a significant challenge to modern radar. Traditional active anti-jamming strategy learning methods often suffer…
Scalable Exploration via Ensemble++
Yingru Li, Jiawei Xu, Baoxiang Wang +1
Thompson Sampling is a principled method for balancing exploration and exploitation, but its real-world adoption faces computational challenges in large-scale or non-conjugate sett…
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation
Yingru Li, Zhi-Quan Luo
This work advances randomized exploration in reinforcement learning (RL) with function approximation modeled by linear mixture MDPs. We establish the first prior-dependent Bayesian…
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games
Yingru Li, Liangqi Liu, Wenqiang Pu +2
This work tackles the complexities of multi-player scenarios in \emph{unknown games}, where the primary challenge lies in navigating the uncertainty of the environment through band…
Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization
Liangqi Liu, Wenqiang Pu, Yingru Li +2
The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sampl…
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
Yingru Li, Jiawei Xu, Lei Han +1
We propose HyperAgent, a reinforcement learning (RL) algorithm based on the hypermodel framework for exploration in RL. HyperAgent allows for the efficient incremental approximatio…