- Conference Paper
In this paper, we establish a theoretical comparison between the asymptotic mean square errors 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 or with linear function approximation, provided that the optimal policy is unique and the algorithms converge. We show that the asymptotic mean-square 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 the output of Double Q-learning is the average of its two estimators. We also present some practical implications of this theoretical observation using simulations. Show more
Book titleAdvances in Neural Information Processing Systems 33
Pages / Article No.
Organisational unit09729 - He, Niao / He, Niao
NotesDue to the Coronavirus (COVID-19) the conference was conducted virtually.
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