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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/24394
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dc.contributor.authorGao, S-
dc.contributor.authorShi, H-
dc.contributor.authorWang, F-
dc.contributor.authorWang, Z-
dc.contributor.authorZhang, S-
dc.contributor.authorLi, Y-
dc.contributor.authorSun, Y-
dc.date.accessioned2022-04-05T14:45:52Z-
dc.date.available2022-04-05T14:45:52Z-
dc.date.issued2022-02-16-
dc.identifier.citationGao, S. et al. (2022) ‘Deterministic policy optimization with clipped value expansion and long-horizon planning’, Neurocomputing, 483, pp. 299 - 310. doi:10.1016/j.neucom.2022.02.022.en_US
dc.identifier.issn0925-2312-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/24394-
dc.description.sponsorshipNational key R&D Program of China (2019YFC1906201); National Natural Science Foundation of China (91748122).en_US
dc.format.extent299 - 310-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectmodel-based reinforcement learningen_US
dc.subjectpolicy gradienten_US
dc.subjectsample efficiencyen_US
dc.subjectplanningen_US
dc.subjectimitation learningen_US
dc.titleDeterministic policy optimization with clipped value expansion and long-horizon planningen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1016/j.neucom.2022.02.022-
dc.relation.isPartOfNeurocomputing-
pubs.publication-statusPublished-
pubs.volume483-
dc.identifier.eissn1872-8286-
Appears in Collections:Dept of Computer Science Embargoed Research Papers

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