The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Saiz langkah ialah parameter kepentingan asas dalam pembelajaran algoritma, terutamanya untuk kaedah kecerunan dasar semula jadi (NPG). Kami memperoleh sempadan atas untuk saiz langkah dalam anggaran NPG tambahan, dan mencadangkan saiz langkah penyesuaian untuk melaksanakan sempadan atas terbitan. Saiz langkah penyesuaian yang dicadangkan menjamin bahawa parameter yang dikemas kini tidak melebihi sasaran, yang dicapai dengan menimbang sampel pembelajaran mengikut kepentingan relatifnya. Kami juga menyediakan sempadan atas dan bawah yang ketat untuk saiz langkah, walaupun ia tidak sesuai untuk pembelajaran tambahan. Kami mengesahkan kegunaan saiz langkah yang dicadangkan menggunakan penanda aras klasik. Untuk pengetahuan terbaik kami, ini ialah kaedah saiz langkah penyesuaian pertama untuk anggaran NPG.
Ryo IWAKI
Osaka University
Hiroki YOKOYAMA
Tamagawa University
Minoru ASADA
Osaka University
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Salinan
Ryo IWAKI, Hiroki YOKOYAMA, Minoru ASADA, "Incremental Estimation of Natural Policy Gradient with Relative Importance Weighting" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 9, pp. 2346-2355, September 2018, doi: 10.1587/transinf.2017EDP7363.
Abstract: The step size is a parameter of fundamental importance in learning algorithms, particularly for the natural policy gradient (NPG) methods. We derive an upper bound for the step size in an incremental NPG estimation, and propose an adaptive step size to implement the derived upper bound. The proposed adaptive step size guarantees that an updated parameter does not overshoot the target, which is achieved by weighting the learning samples according to their relative importances. We also provide tight upper and lower bounds for the step size, though they are not suitable for the incremental learning. We confirm the usefulness of the proposed step size using the classical benchmarks. To the best of our knowledge, this is the first adaptive step size method for NPG estimation.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7363/_p
Salinan
@ARTICLE{e101-d_9_2346,
author={Ryo IWAKI, Hiroki YOKOYAMA, Minoru ASADA, },
journal={IEICE TRANSACTIONS on Information},
title={Incremental Estimation of Natural Policy Gradient with Relative Importance Weighting},
year={2018},
volume={E101-D},
number={9},
pages={2346-2355},
abstract={The step size is a parameter of fundamental importance in learning algorithms, particularly for the natural policy gradient (NPG) methods. We derive an upper bound for the step size in an incremental NPG estimation, and propose an adaptive step size to implement the derived upper bound. The proposed adaptive step size guarantees that an updated parameter does not overshoot the target, which is achieved by weighting the learning samples according to their relative importances. We also provide tight upper and lower bounds for the step size, though they are not suitable for the incremental learning. We confirm the usefulness of the proposed step size using the classical benchmarks. To the best of our knowledge, this is the first adaptive step size method for NPG estimation.},
keywords={},
doi={10.1587/transinf.2017EDP7363},
ISSN={1745-1361},
month={September},}
Salinan
TY - JOUR
TI - Incremental Estimation of Natural Policy Gradient with Relative Importance Weighting
T2 - IEICE TRANSACTIONS on Information
SP - 2346
EP - 2355
AU - Ryo IWAKI
AU - Hiroki YOKOYAMA
AU - Minoru ASADA
PY - 2018
DO - 10.1587/transinf.2017EDP7363
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2018
AB - The step size is a parameter of fundamental importance in learning algorithms, particularly for the natural policy gradient (NPG) methods. We derive an upper bound for the step size in an incremental NPG estimation, and propose an adaptive step size to implement the derived upper bound. The proposed adaptive step size guarantees that an updated parameter does not overshoot the target, which is achieved by weighting the learning samples according to their relative importances. We also provide tight upper and lower bounds for the step size, though they are not suitable for the incremental learning. We confirm the usefulness of the proposed step size using the classical benchmarks. To the best of our knowledge, this is the first adaptive step size method for NPG estimation.
ER -