Finite-Time Analysis of Asynchronous Q-Learning with Discrete-Time Switching System Models

• URL: http://arxiv.org/abs/2102.08583v2
• Date: Fri, 19 Feb 2021 15:52:19 GMT
• Title: Finite-Time Analysis of Asynchronous Q-Learning with Discrete-Time Switching System Models
• Authors: Donghwan Lee
• Abstract summary: We prove that Q-learning with a constant step-size can be naturally formulated as discrete-time switched linear systems. It offers novel and intuitive insights on Q-learning mainly based on controloretic frameworks.
• Score: 6.85316573653194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper develops a novel framework to analyze the convergence of Q-learning algorithm from a discrete-time switching system perspective. We prove that asynchronous Q-learning with a constant step-size can be naturally formulated as discrete-time stochastic switched linear systems. It offers novel and intuitive insights on Q-learning mainly based on control theoretic frameworks. For instance, the proposed analysis explains the overestimation phenomenon in Q-learning due to the maximization bias. Based on the control system theoretic argument and some nice structures of Q-learning, a new finite-time analysis of the Q-learning is given with a novel error bound.

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