๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance

โœ Scribed by Cang Ye; Yung, N.H.C.; Danwei Wang


Book ID
117938046
Publisher
IEEE
Year
2003
Tongue
English
Weight
1017 KB
Volume
33
Category
Article
ISSN
1083-4419

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


A reinforcement learning adaptive fuzzy
โœ Chuan-Kai Lin ๐Ÿ“‚ Article ๐Ÿ“… 2003 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 233 KB

In this paper, a new reinforcement learning scheme is developed for a class of serial-link robot arms. Traditional reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment. In the proposed reinforcement learning

Learning fuzzy rules for controllers wit
โœ T. Pal; N. R. Pal; M. Pal ๐Ÿ“‚ Article ๐Ÿ“… 2003 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 154 KB

A genetic algorithm (GA)-based scheme for learning fuzzy rules for controllers, called an optimized fuzzy logic controller (OFLC) was proposed by Chan, Xie and Rad (2000). In this article we first analyze their OFLC and discuss some of its limitations. We also propose some modifications on an OFLC t

Reinforcement learning combined with a f
โœ K.H. Quah; C. Quek; G. Leedham ๐Ÿ“‚ Article ๐Ÿ“… 2005 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 222 KB

Reinforcement learning has been widely-used for applications in planning, control, and decision making. Rather than using instructive feedback as in supervised learning, reinforcement learning makes use of evaluative feedback to guide the learning process. In this paper, we formulate a pattern class