کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
383255 660814 2016 17 صفحه PDF سفارش دهید دانلود کنید
عنوان انگلیسی مقاله
Extracting bottlenecks for reinforcement learning agent by holonic concept clustering and attentional functions
ترجمه فارسی عنوان
استخراج تنگناها برای عامل یادگیری تقویت توسط خوشه بندی مفهوم هولونیک و توابع توجه
کلمات کلیدی
یادگیری تقویت ؛ انتزاع - مفهوم - برداشت؛ مفهوم؛ خوشه بندی هولنیک/سلسله مراتبی؛ توجه
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• Attentional functions are introduced to extract bottlenecks indirectly.
• The results showed a considerable improvement in the precision of detection.
• It has better time complexity comparing to other methods.
• It needs fewer requirements for designer's help comparing to other methods.

Reinforcement learning is not well scalable in state spaces with high-dimensions. The hierarchical reinforcement learning resolves this problem by task decomposition. Task decomposition is done by extracting bottlenecks, which is in turn another challenging issue, especially in terms of time and memory complexity and the need to the prior knowledge of the environment. To alleviate these issues, a new approach is proposed toward the problem of extracting bottlenecks. Holonic concept clustering and attentional functions are proposed to extract bottleneck states. To this end, states are organized based on the effects of actions by means of a holonic clustering to extract high-level concepts. High-level concepts are used as cues for controlling attention. The proposed mechanism has a better time complexity and fewer requirements to the designer's help. The experimental results showed a considerable improvement in the precision of bottleneck detection and agent's performance for traditional benchmarks comparing to other similar methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 54, 15 July 2016, Pages 61–77
نویسندگان
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