Papers › The Mean-Squared Error of Double Q-Learning

The Mean-Squared Error of Double Q-Learning

9 Jul 2020NeurIPS 2020 12arXiv:2007.05034archive 2025-07-28

Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant

In this paper, we establish a theoretical comparison between the asymptotic mean-squared error of Double Q-learning and Q-learning. Our result builds upon an analysis for linear stochastic approximation based on Lyapunov equations and applies to both tabular setting and with linear function approximation, provided that the optimal policy is unique and the algorithms converge. We show that the asymptotic mean-squared error of Double Q-learning is exactly equal to that of Q-learning if Double Q-learning uses twice the learning rate of Q-learning and outputs the average of its two estimators. We also present some practical implications of this theoretical observation using simulations.

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