256 citations · 505 across the 13 of their papers we have counts for
21 papers
Settling the Horizon-Dependence of Sample Complexity in Reinforcement Learning
Yuanzhi Li, Ruosong Wang, Lin F. Yang
Recently there is a surge of interest in understanding the horizon-dependence of the sample complexity in reinforcement learning (RL). Notably, for an RL environment with horizon l…
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap
Yuanhao Wang, Ruosong Wang, Sham M. Kakade
A fundamental question in the theory of reinforcement learning is: suppose the optimal -function lies in the linear span of a given dimensional feature mapping, is sample-ef…
Bilinear Classes: A Structural Framework for Provable Generalization in RL
Simon S. Du, Sham M. Kakade, Jason D. Lee +4
This work introduces Bilinear Classes, a new structural framework, which permit generalization in reinforcement learning in a wide variety of settings through the use of function a…
Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension
Ruosong Wang, Ruslan Salakhutdinov, Lin F. Yang
Value function approximation has demonstrated phenomenal empirical success in reinforcement learning (RL). Nevertheless, despite a handful of recent progress on developing theory f…
Nearly Linear Row Sampling Algorithm for Quantile Regression
Yi Li, Ruosong Wang, Lin Yang +1
We give a row sampling algorithm for the quantile loss function with sample complexity nearly linear in the dimensionality of the data, improving upon the previous best algorithm w…
Preference-based Reinforcement Learning with Finite-Time Guarantees
Yichong Xu, Ruosong Wang, Lin F. Yang +2
Preference-based Reinforcement Learning (PbRL) replaces reward values in traditional reinforcement learning by preferences to better elicit human opinion on the target objective, e…