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Poster
FiniteTime Analysis for Double Qlearning
Huaqing Xiong · Lin Zhao · Yingbin Liang · Wei Zhang
Although Qlearning is one of the most successful algorithms for finding the best actionvalue function (and thus the optimal policy) in reinforcement learning, its implementation often suffers from large overestimation of Qfunction values incurred by random sampling. The double Qlearning algorithm proposed in~\citet{hasselt2010double} overcomes such an overestimation issue by randomly switching the update between two Qestimators, and has thus gained significant popularity in practice. However, the theoretical understanding of double Qlearning is rather limited. So far only the asymptotic convergence has been established, which does not characterize how fast the algorithm converges. In this paper, we provide the first nonasymptotic (i.e., finitetime) analysis for double Qlearning. We show that both synchronous and asynchronous double Qlearning are guaranteed to converge to an $\epsilon$accurate neighborhood of the global optimum by taking
$\tilde{\Omega}\left(\left( \frac{1}{(1\gamma)^6\epsilon^2}\right)^{\frac{1}{\omega}} +\left(\frac{1}{1\gamma}\right)^{\frac{1}{1\omega}}\right)$ iterations, where $\omega\in(0,1)$ is the decay parameter of the learning rate, and $\gamma$ is the discount factor. Our analysis develops novel techniques to derive finitetime bounds on the difference between two interconnected stochastic processes, which is new to the literature of stochastic approximation.
Author Information
Huaqing Xiong (Ohio State University)
Lin Zhao (National University of Singapore)
Yingbin Liang (The Ohio State University)
Wei Zhang (Southern University of Science and Technology)
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