A Reinforcement Learning Model Using Neural Networks for Music Sight Reading Learning Problem

TL;DR

This paper presents a neural network-based reinforcement learning model for music sight reading, focusing on how the brain learns and adjusts synaptic weights during the process.

Contribution

It introduces a novel neural network reinforcement learning model with an updated weight adjustment equation for music sight reading.

Findings

Model demonstrates effective learning of sight reading tasks

Weight adjustment improves learning accuracy

Provides insights into neural mechanisms of music sight reading

Abstract

Music Sight Reading is a complex process in which when it is occurred in the brain some learning attributes would be emerged. Besides giving a model based on actor-critic method in the Reinforcement Learning, the agent is considered to have a neural network structure. We studied on where the sight reading process is happened and also a serious problem which is how the synaptic weights would be adjusted through the learning process. The model we offer here is a computational model on which an updated weights equation to fix the weights is accompanied too.