9 papers
Q-Learning with Fine-Grained Gap-Dependent Regret
Haochen Zhang, Zhong Zheng, Lingzhou Xue
We study fine-grained gap-dependent regret bounds for model-free reinforcement learning in episodic tabular Markov Decision Processes. Existing model-free algorithms achieve minima…
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
Haochen Zhang, Zhong Zheng, Lingzhou Xue
Motivated by real-world settings where data collection and policy deployment -- whether for a single agent or across multiple agents -- are costly, we study the problem of on-polic…
Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
Haochen Zhang, Zhong Zheng, Lingzhou Xue
We study gap-dependent performance guarantees for nearly minimax-optimal algorithms in reinforcement learning with linear function approximation. While prior works have established…
Zero-Shot Transfer Capabilities of the Sundial Foundation Model for Leaf Area Index Forecasting
Peining Zhang, Hongchen Qin, Haochen Zhang +3
This work investigates the zero-shot forecasting capability of time series foundation models for Leaf Area Index (LAI) forecasting in agricultural monitoring. Using the HiQ dataset…
Gap-Dependent Bounds for Federated -learning
Haochen Zhang, Zhong Zheng, Lingzhou Xue
We present the first gap-dependent analysis of regret and communication cost for on-policy federated -Learning in tabular episodic finite-horizon Markov decision processes (MDPs…
Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring
Xinyuan Tu
Few-shot learning (FSL) has shown promise in vision but remains largely unexplored for \emph{industrial} time-series data, where annotating every new defect is prohibitively expens…