کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8406794 | 1544961 | 2016 | 34 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Robotic action acquisition with cognitive biases in coarse-grained state space
ترجمه فارسی عنوان
گرفتن اقدام روباتیک با تعصب شناختی در فضای حالت درشت دانه
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
مدلسازی و شبیه سازی
چکیده انگلیسی
Some of the authors have previously proposed a cognitively inspired reinforcement learning architecture (LS-Q) that mimics cognitive biases in humans. LS-Q adaptively learns under uniform, coarse-grained state division and performs well without parameter tuning in a giant-swing robot task. However, these results were shown only in simulations. In this study, we test the validity of the LS-Q implemented in a robot in a real environment. In addition, we analyze the learning process to elucidate the mechanism by which the LS-Q adaptively learns under the partially observable environment. We argue that the LS-Q may be a versatile reinforcement learning architecture, which is, despite its simplicity, easily applicable and does not require well-prepared settings.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biosystems - Volume 145, July 2016, Pages 41-52
Journal: Biosystems - Volume 145, July 2016, Pages 41-52
نویسندگان
Daisuke Uragami, Yu Kohno, Tatsuji Takahashi,