کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
390712 661294 2009 24 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Reinforcement distribution in fuzzy Q-learning
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Reinforcement distribution in fuzzy Q-learning
چکیده انگلیسی

Q-learning is one of the most popular reinforcement learning methods that allows an agent to learn the relationship between interval-valued state and action spaces, through a direct interaction with the environment. Fuzzy Q-learning is an extension to this algorithm to enable it to evolve fuzzy inference systems (FIS) which range on continuous state and action spaces. In a FIS, the interaction among fuzzy rules plays a primary role to achieve good performance and robustness. Learning a system where this interaction is present gives to the learning mechanism problems due to eventually incoherent reinforcements coming to the same rule due to its interaction with other rules. In this paper, we will introduce different strategies to distribute reinforcement to reduce this undesired effect and to stabilize the obtained reinforcement. In particular, we will present two strategies: the former focuses on rewarding the actions chosen by each rule during the cooperation phase, the latter on rewarding the rules presenting actions closer to those actually executed rather than the rules that contributed to generate such actions.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Fuzzy Sets and Systems - Volume 160, Issue 10, 16 May 2009, Pages 1420-1443