Finite-Time Error Bounds for Greedy-GQ

arXiv:2209.02555v2 Announce Type: replace
Abstract: Greedy-GQ with linear function approximation, originally proposed in \cite{maei2010toward}, is a value-based off-policy algorithm for optimal control in reinforcement learning, and it has a non-linear two timescale structure with the non-convex objective function. This paper develops its tightest finite-time error bounds. We show that the Greedy-GQ algorithm converges as fast as $\mathcal{O}({1}/{\sqrt{T}})$ under the i.i.d.\ setting and $\mathcal{O}({\log T}/{\sqrt{T}})$ under the Markovian setting. We further design a variant of the vanilla Greedy-GQ algorithm using the …

abstract algorithm approximation arxiv control cs.lg error function linear non-linear paper policy reinforcement reinforcement learning show timescale type value