20 citations · 52 across the 9 of their papers we have counts for
3 papers · 1 filter
Online RL in Linearly -Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore
Gellért Weisz, András György, Csaba Szepesvári
We consider online reinforcement learning (RL) in episodic Markov decision processes (MDPs) under the linear -realizability assumption, where it is assumed that the action-val…
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL
Qinghua Liu, Gellért Weisz, András György +2
While policy optimization algorithms have played an important role in recent empirical success of Reinforcement Learning (RL), the existing theoretical understanding of policy opti…
Exponential Hardness of Reinforcement Learning with Linear Function Approximation
Daniel Kane, Sihan Liu, Shachar Lovett +3
A fundamental question in reinforcement learning theory is: suppose the optimal value functions are linear in given features, can we learn them efficiently? This problem's counterp…