Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling

TL;DR

This paper analyzes the finite-time convergence rates of stochastic approximation algorithms with delayed updates under Markovian sampling, providing tight bounds and a delay-adaptive scheme that improves convergence speed.

Contribution

It introduces a tight finite-time analysis of delayed stochastic approximation under Markovian sampling and proposes a delay-adaptive scheme that scales with average delay, requiring no prior delay knowledge.

Findings

Exponential convergence of last iterate to a neighborhood of the fixed point.

Tight bounds depend on maximum delay and mixing time.

Delay-adaptive scheme scales convergence with average delay.

Abstract

Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updates under Markovian sampling. While the effect of delays has been extensively studied for optimization, the manner in which they interact with the underlying Markov process to shape the finite-time performance of SA remains poorly understood. In this context, our first main contribution is to show that under time-varying bounded delays, the delayed SA update rule guarantees exponentially fast convergence of the \emph{last iterate} to a ball around the SA operator's fixed point. Notably, our bound is \emph{tight} in its dependence on both the maximum delay $\tau_{max}$, and the mixing time $\tau_{mix}$. To achieve this tight bound, we develop a novel inductive proof technique that, unlike various existing delayed-optimization analyses, relies on establishing uniform boundedness of the iterates. As such, our proof may be of independent interest. Next, to mitigate the impact of the maximum delay on the convergence rate, we provide the first finite-time analysis of a delay-adaptive SA scheme under Markovian sampling. In particular, we show that the exponent of convergence of this scheme gets scaled down by $\tau_{avg}$, as opposed to $\tau_{max}$ for the vanilla delayed SA rule; here, $\tau_{avg}$ denotes the average delay across all iterations. Moreover, the adaptive scheme requires no prior knowledge of the delay sequence for step-size tuning. Our theoretical findings shed light on the finite-time effects of delays for a broad class of algorithms, including TD learning, Q-learning, and stochastic gradient descent under Markovian sampling.