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arXiv · 2002.00260

Finite-Time Analysis of Asynchronous Stochastic Approximation and $Q$-Learning

Guannan Qu·Adam Wierman
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Abstract

We consider a general asynchronous Stochastic Approximation (SA) scheme featuring a weighted infinity-norm contractive operator, and prove a bound on its finite-time convergence rate on a single trajectory. Additionally, we specialize the result to asynchronous $Q$-learning. The resulting bound matches the sharpest available bound for synchronous $Q$-learning, and improves over previous known bounds for asynchronous $Q$-learning.

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Guannan Qu, Adam Wierman. 2020-02-01. Finite-Time Analysis of Asynchronous Stochastic Approximation and $Q$-Learning. https://arxiv.org/abs/2002.00260

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