Hauptseite > Publikationsdatenbank > Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization > print

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024 7 _ |a 10.1016/j.neunet.2023.01.025
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024 7 _ |a 0893-6080
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024 7 _ |a 1879-2782
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037 _ _ |a FZJ-2024-01831
082 _ _ |a 004
100 1 _ |a Xing, Jinwei
|0 0000-0002-8085-2769
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|e Corresponding author
245 _ _ |a Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization
260 _ _ |a Amsterdam
|c 2023
|b Elsevier
336 7 _ |a article
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336 7 _ |a ARTICLE
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700 1 _ |a Nagata, Takashi
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700 1 _ |a Zou, Xinyun
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700 1 _ |a Neftci, Emre
|0 P:(DE-Juel1)188273
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700 1 _ |a Krichmar, Jeffrey L.
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773 _ _ |a 10.1016/j.neunet.2023.01.025
|g Vol. 161, p. 228 - 241
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|p 228 - 241
|t Neural networks
|v 161
|y 2023
|x 0893-6080
909 C O |o oai:juser.fz-juelich.de:1023826
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|v Neuromorphic Computing and Network Dynamics
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914 1 _ |y 2024
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