Computational Model of Music Sight Reading: A Reinforcement Learning Approach

TL;DR

This paper introduces a reinforcement learning-based computational model for music sight reading, emphasizing efficient policy learning without complex value function computations, and providing a normative behavioral framework for agent-environment interaction.

Contribution

It presents a novel reinforcement learning approach tailored for music sight reading, avoiding complex value function calculations and utilizing a modified POMDP for faster learning.

Findings

Efficient policy derivation without complex value functions

Faster learning of state-action pairs in agents

A normative model for agent interaction with musical environment

Abstract

Although the Music Sight Reading process has been studied from the cognitive psychology view points, but the computational learning methods like the Reinforcement Learning have not yet been used to modeling of such processes. In this paper, with regards to essential properties of our specific problem, we consider the value function concept and will indicate that the optimum policy can be obtained by the method we offer without to be getting involved with computing of the complex value functions. Also, we will offer a normative behavioral model for the interaction of the agent with the musical pitch environment and by using a slightly different version of Partially observable Markov decision processes we will show that our method helps for faster learning of state-action pairs in our implemented agents.