文献
J-GLOBAL ID:202102223002005388
整理番号:21A0009491
確率的環境における効率的なポリシー最適化のための混合強化学習【JST・京大機械翻訳】
Mixed Reinforcement Learning for Efficient Policy Optimization in Stochastic Environments
著者 (8件):
Mu Yao
(Tsinghua University,School of Vehicle and Mobility,State Key Laboratory of Automotive Safety and Energy,Beijing,China,100084)
,
Peng Baiyu
(Tsinghua University,School of Vehicle and Mobility,State Key Laboratory of Automotive Safety and Energy,Beijing,China,100084)
,
Gu Ziqing
(Tsinghua University,School of Vehicle and Mobility,State Key Laboratory of Automotive Safety and Energy,Beijing,China,100084)
,
Li Shengbo Eben
(Tsinghua University,School of Vehicle and Mobility,State Key Laboratory of Automotive Safety and Energy,Beijing,China,100084)
,
Liu Chang
(Cornell University,Sibley School of Mechanical and Aerospace Engineering,New York,USA,14853)
,
Nie Bingbing
(Tsinghua University,School of Vehicle and Mobility,State Key Laboratory of Automotive Safety and Energy,Beijing,China,100084)
,
Zheng Jianfeng
(Smart Transportation Division,Didi Chuxing,China,100000)
,
Zhang Bo
(Smart Transportation Division,Didi Chuxing,China,100000)
資料名:
IEEE Conference Proceedings
(IEEE Conference Proceedings)
巻:
2020
号:
ICCAS
ページ:
1212-1219
発行年:
2020年
JST資料番号:
W2441A
資料種別:
会議録 (C)
記事区分:
原著論文
発行国:
アメリカ合衆国 (USA)
言語:
英語 (EN)