بهبود سرعت آموزش در مسائل یادگیری تقویتی مبتنی بر انتقال دانش عصبی‌فازی

نوع مقاله: علمی-پژوهشی

نویسندگان

دانشکده مهندسی کامپیوتر - دانشگاه علم و هنر

چکیده

این مقاله به موضوع انتقال یادگیری در محیط­هایی که بعضی از ویژگی­های آن مشترک است می­پردازد. چالش اصلی در این مبحث، نحوه انتقال دانش به­دست­آمده از محیط مبدأ به محیط مقصد است. در ایده ارائه­شده با در نظر­گرفتن ویژگی­های مشترک در فضای عامل بین دو محیط، ابتدا مقدار ارزش - عمل در محیط مبدأ به­دست می­آید، سپس از یک شبکه عصبی- فازی برای تقریب مقدار تابع ارزش - عمل بهره برده می‏شود. در محیط مقصد، مقدار ارزش - عمل از ترکیب مقدار پیش­بینی شبکه عصبی - فازی و مقدار به­دست­آمده در خود آن محیط استفاده می­شود. به‌عبارت دیگر با توجه به آموزش انجام‌شده در محیط مبدأ، مقادیر ارزش - عمل در محیط مقصد از ترکیب مقادیر ارزش - عمل تقریب­زده­شده توسط شبکه عصبی - فازی و مقدار به­دست آمده از الگوریتم یادگیری در آن محیط به­دست می­آید. شایان ذکر است که از الگوریتم یادگیری Q در محیط استفاده‌شده است. نتایج حاصل از ایده ارائه‌شده، حاکی از افزایش چشمگیر سرعت یادگیری می­باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Improving the learning speed in reinforcement learning issues based on the transfer learning of neuro-fuzzy knowledge

نویسندگان [English]

  • F. Saadatjoo
  • E. Ghandehari
Computer Engineering Department, Science and Art University, Yazd, Iran
چکیده [English]

This paper to the topic of transfer learning in environments that share some of its features. The main challenge in this topic is how to transfer knowledge from the source environment to the target environment. In the presented idea, taking into account the common features in the operating space between the two environments, the value of the operation in the source environment first is obtained and then it uses a neuro -fuzzy network to approximate the value of the value function of the operation. In the target environment, the value of the mode of operation is used to combine the predictive value of the neuro - fuzzy network and the amount received in the environment itself. In other words, according to the training carried out in the source environment, value-action values ​​in the target environment are derived from the combination of value-action values ​​approximated by the neuro - fuzzy network and the amount obtained from the learning algorithm in that environment. It is worth noting that the learning algorithm Q is used in the environment. The results of the proposed idea indicate a significant increase in learning speed.

کلیدواژه‌ها [English]

  • reinforcement learning
  • transfer knowledge
  • common features
  • neuro - fuzzy network
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