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arXiv · 2102.08583

A Discrete-Time Switching System Analysis of Q-learning

Donghwan Lee·Jianghai Hu·Niao He
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Abstract

This paper develops a novel control-theoretic framework to analyze the non-asymptotic convergence of Q-learning. We show that the dynamics of asynchronous Q-learning with a constant step-size can be naturally formulated as a discrete-time stochastic affine switching system. Moreover, the evolution of the Q-learning estimation error is over- and underestimated by trajectories of two simpler dynamical systems. Based on these two systems, we derive a new finite-time error bound of asynchronous Q-learning when a constant stepsize is used. Our analysis also sheds light on the overestimation phenomenon of Q-learning. We further illustrate and validate the analysis through numerical simulations.

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Donghwan Lee, Jianghai Hu, Niao He. 2021-02-17. A Discrete-Time Switching System Analysis of Q-learning. https://doi.org/10.1137/22m1489976

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