  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.
