Reinforcement learning for ART-based fuzzy adaptive learning control networks

Cheng‐Jian Lin, Chin-Teng Lin
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引用次数: 15

Abstract

This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON) for solving various reinforcement learning problems. The proposed RFALCON is constructed by integrating two fuzzy adaptive learning control networks (FALCON), each of which is a connectionist model with a feedforward multilayered network developed for the realization of a fuzzy logic controller. An online structure/parameter learning algorithm, called RFALCON-ART, is proposed for constructing the RFALCON dynamically. The proposed RFALCON also preserves the advantages of the original FALCON, such as the ability to do online partition the input/output spaces, tune membership functions, and find proper fuzzy logic rules. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first reinforcement signal arrives. The users thus need not give it any a priori knowledge or even any initial information on these.<>
基于art的模糊自适应学习控制网络的强化学习
针对各种强化学习问题,提出了一种强化模糊自适应学习控制网络(RFALCON)。本文提出的RFALCON是通过集成两个模糊自适应学习控制网络(FALCON)来构建的,每个网络都是一个连接主义模型,并为实现模糊逻辑控制器而开发了一个前馈多层网络。提出了一种动态构造RFALCON的在线结构/参数学习算法RFALCON- art。提出的RFALCON还保留了原始FALCON的优点,例如能够在线划分输入/输出空间、调整隶属函数和找到适当的模糊逻辑规则。在其初始形式中,没有隶属函数、模糊划分和模糊逻辑规则。当第一个强化信号到达时,它们就会产生并开始生长。因此,用户不需要向它提供任何先验知识,甚至不需要提供任何关于这些的初始信息。
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