Reward effect in reinforcement learning systems
المؤلفون المشاركون
Bashir, Lubna Zaghlul
Walid, Zaynah
المصدر
Iraqi Journal of Computer, Communications and Control Engineering
العدد
المجلد 12، العدد 1 (30 يونيو/حزيران 2012)، ص ص. 69-95، 27ص.
الناشر
تاريخ النشر
2012-06-30
دولة النشر
العراق
عدد الصفحات
27
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Learning Classifier Systems (LCS), are a machine learning technique which combines reinforcement learning, evolutionary computing and other heuristics to produce adaptive systems.
The system HRC (Human –Rat- Cheese) focuses in creating artificial creature (Rat) using computer simulation, and learning it how to choose between two different basic behaviors, (approach / escape) combining them to perform complex behavior, which represents the final response in changing environment.
The HRC is built of two-classifier subsystems working together, each classifier system learns a simple behavior, and the system as a whole has as its learning goal the control of activities.
Flat architecture was used.
The flat organization allows distinguishing between two different learning activities : the learning of basic behavior and the learning of switch behavior.
One classifier system learns basic behavior, (approach / escape), i.e., it is used to learn the simulated robot single step movement in every direction in the environment.
Whereas the other classifier system learns to control the activities of basic classifier systems, i.e., it is used to learn to choose between basic behaviors using suppression as a composition mechanism to chose between two basic behaviors which represent complex behavior.
Simple experiments were executed for HRC : comparing and contrasting the effect of the reinforcement learning using reward & punishment with learning using reward only.
Experiment results show that the run using reinforcement learning with reward only is unable to perform as well as the run with reinforcement learning with reward and punishment.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Bashir, Lubna Zaghlul& Walid, Zaynah. 2012. Reward effect in reinforcement learning systems. Iraqi Journal of Computer, Communications and Control Engineering،Vol. 12, no. 1, pp.69-95.
https://search.emarefa.net/detail/BIM-320370
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Bashir, Lubna Zaghlul& Walid, Zaynah. Reward effect in reinforcement learning systems. Iraqi Journal of Computer, Communications and Control Engineering Vol. 12, no. 1 (2012), pp.69-95.
https://search.emarefa.net/detail/BIM-320370
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Bashir, Lubna Zaghlul& Walid, Zaynah. Reward effect in reinforcement learning systems. Iraqi Journal of Computer, Communications and Control Engineering. 2012. Vol. 12, no. 1, pp.69-95.
https://search.emarefa.net/detail/BIM-320370
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes appendices : p. 80-95
رقم السجل
BIM-320370
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر