Pattern Recognition Based on Variant Reinforcement Meta-learning

Abstract

Pattern recognition is the study of automatic processing and interpretation of patterns using computers through mathematical techniques, where the environment and the objects are collectively referred to as “patterns”. It studies the problem of classifying samples by their attributes using computational methods. The main research directions of pattern recognition are computer vision, image processing, and natural language processing. In this paper, we discuss the application of a variant of reinforcement meta-learning, which combines experience replay and meta-learning in reinforcement learning, to the problem of continuous learning in pattern recognition. It also compares its accuracy with current solutions such as online learning for the continuous learning problem in pattern recognition. It mainly consists of reinforcement learning, reptilian meta-learning and experience replay in SGDM optimizer. It is finally concluded that for the problem of continuous learning in pattern recognition, its ability to preserve past learned knowledge with rapid learning of current knowledge is higher than the current online learning. Based on this, the effect of different parameters on the performance of this variant of reinforcement meta-learning is explored.