2 citations · 2 across the 4 of their papers we have counts for
4 papers
List Replicable Reinforcement Learning
Bohan Zhang, Michael Chen, A. Pavan +3
Replicability is a fundamental challenge in reinforcement learning (RL), as RL algorithms are empirically observed to be unstable and sensitive to variations in training conditions…
Misspecified -Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error
Ally Yalei Du, Lin F. Yang, Ruosong Wang
The recent work by Dong & Yang (2023) showed for misspecified sparse linear bandits, one can obtain an -optimal policy using a polynomial number of samples when th…
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning
Junyan Liu, Yunfan Li, Ruosong Wang +1
Existing metrics for reinforcement learning (RL) such as regret, PAC bounds, or uniform-PAC (Dann et al., 2017), typically evaluate the cumulative performance, while allowing the a…
Online Sub-Sampling for Reinforcement Learning with General Function Approximation
Dingwen Kong, Ruslan Salakhutdinov, Ruosong Wang +1
Most of the existing works for reinforcement learning (RL) with general function approximation (FA) focus on understanding the statistical complexity or regret bounds. However, the…