Campus Units

Industrial and Manufacturing Systems Engineering, Electrical and Computer Engineering, Bioeconomy Institute (BEI)

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

2021

Journal or Book Title

arXiv

Research Focus Area(s)

​Operations Research

Abstract

Genomic selection (GS) is a technique that plant breeders use to select individuals to mate and produce new generations of species. Allocation of resources is a key factor in GS. At each selection cycle, breeders are facing the choice of budget allocation to make crosses and produce the next generation of breeding parents. Inspired by recent advances in reinforcement learning for AI problems, we develop a reinforcement learning-based algorithm to automatically learn to allocate limited resources across different generations of breeding. We mathematically formulate the problem in the framework of Markov Decision Process (MDP) by defining state and action spaces. To avoid the explosion of the state space, an integer linear program is proposed that quantifies the trade-off between resources and time. Finally, we propose a value function approximation method to estimate the action-value function and then develop a greedy policy improvement technique to find the optimal resources. We demonstrate the effectiveness of the proposed method in enhancing genetic gain using a case study with realistic data.

Comments

This is a pre-print of the article Moeinizade, Saba, Guiping Hu, and Lizhi Wang. "A reinforcement learning approach to resource allocation in genomic selection." arXiv preprint arXiv:2107.10901 (2021). Posted with permission.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

Published Version

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