Finite-Time Analysis of Asynchronous Stochastic Approximation and $Q$-Learning
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Publication:6333942
arXiv2002.00260MaRDI QIDQ6333942
Author name not available (Why is that?)
Publication date: 1 February 2020
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 -learning. The resulting bound matches the sharpest available bound for synchronous -learning, and improves over previous known bounds for asynchronous -learning.
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