DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Donghwan | ko |
dc.contributor.author | He, Niao | ko |
dc.date.accessioned | 2020-12-18T07:30:17Z | - |
dc.date.available | 2020-12-18T07:30:17Z | - |
dc.date.created | 2020-11-24 | - |
dc.date.created | 2020-11-24 | - |
dc.date.created | 2020-11-24 | - |
dc.date.issued | 2020-12-07 | - |
dc.identifier.citation | 34th Conference on Neural Information Processing Systems, NeurIPS 2020 | - |
dc.identifier.uri | http://hdl.handle.net/10203/278702 | - |
dc.description.abstract | This 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.language | English | - |
dc.publisher | Conference on Neural Information Processing Systems | - |
dc.title | A unified switching system perspective and convergence analysis of Q-learning algorithms | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85102145972 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 34th Conference on Neural Information Processing Systems, NeurIPS 2020 | - |
dc.identifier.conferencecountry | CN | - |
dc.identifier.conferencelocation | Virtual | - |
dc.contributor.localauthor | Lee, Donghwan | - |
dc.contributor.nonIdAuthor | He, Niao | - |
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