Showing 2024Show all
3 papers · 1 filter
stat.ML2024
Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs
Kevin Tan, Wei Fan, Yuting Wei
Hybrid Reinforcement Learning (RL), where an agent learns from both an offline dataset and online explorations in an unknown environment, has garnered significant recent interest.…
cs.DS2024
Distributed Least Squares in Small Space via Sketching and Bias Reduction
Sachin Garg, Kevin Tan, Michał Dereziński
Matrix sketching is a powerful tool for reducing the size of large data matrices. Yet there are fundamental limitations to this size reduction when we want to recover an accurate e…
cs.LG2024
A Natural Extension To Online Algorithms For Hybrid RL With Limited Coverage
Kevin Tan, Ziping Xu
Hybrid Reinforcement Learning (RL), leveraging both online and offline data, has garnered recent interest, yet research on its provable benefits remains sparse. Additionally, many…