Fuzzy Controller of Reward of Reinforcement Learning For Handwritten Digit Recognition

TL;DR

This paper proposes a fuzzy controller to optimize reward in reinforcement learning, improving handwritten digit recognition accuracy by training an actor with font samples and testing on datasets.

Contribution

Introduction of a fuzzy controller to enhance reward optimization in reinforcement learning for handwritten digit recognition.

Findings

Recognition accuracy improved with fuzzy reward control

Fuzzy controller led to better training outcomes

Recognition performance increased on test datasets

Abstract

Recognition of human environment with computer systems always was a big deal in artificial intelligence. In this area handwriting recognition and conceptualization of it to computer is an important area in it. In the past years with growth of machine learning in artificial intelligence, efforts to using this technique increased. In this paper is tried to using fuzzy controller, to optimizing amount of reward of reinforcement learning for recognition of handwritten digits. For this aim first a sample of every digit with 10 standard computer fonts, given to actor and then actor is trained. In the next level is tried to test the actor with dataset and then results show improvement of recognition when using fuzzy controller of reinforcement learning.