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

Guannan Qu, Adam Wierman

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Subject areas: Reinforcement learning, Online learning

Presented in: Session 1B, Session 1D

[Zoom link for poster in Session 1B], [Zoom link for poster in Session 1D]

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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