A unified switching system perspective and convergence analysis of Q-learning algorithms

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dc.contributor.authorLee, Donghwanko
dc.contributor.authorHe, Niaoko
dc.date.accessioned2020-12-18T07:30:17Z-
dc.date.available2020-12-18T07:30:17Z-
dc.date.created2020-11-24-
dc.date.created2020-11-24-
dc.date.issued2020-12-07-
dc.identifier.citation34th Conference on Neural Information Processing Systems, NeurIPS 2020-
dc.identifier.urihttp://hdl.handle.net/10203/278702-
dc.description.abstractThis paper develops a novel and unified framework to analyze the convergence of a large family of Q-learning algorithms from the switching system perspective. We show that the nonlinear ODE models associated with Q-learning and many of its variants can be naturally formulated as affine switching systems. Building on their asymptotic stability, we obtain a number of interesting results: (i) we provide a simple ODE analysis for the convergence of asynchronous Q-learning under relatively weak assumptions; (ii) we establish the first convergence analysis of the averaging Q-learning algorithm, and (iii) we derive a new sufficient condition for the convergence of Q-learning with linear function approximation.-
dc.languageEnglish-
dc.publisherConference on Neural Information Processing Systems-
dc.titleA unified switching system perspective and convergence analysis of Q-learning algorithms-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.publicationname34th Conference on Neural Information Processing Systems, NeurIPS 2020-
dc.identifier.conferencecountryCN-
dc.identifier.conferencelocationVirtual-
dc.contributor.localauthorLee, Donghwan-
dc.contributor.nonIdAuthorHe, Niao-
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EE-Conference Papers(학술회의논문)
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