Title: A reinforcement learning approach to resource allocation in genomic selection Show Chinese title

Title: 基于强化学习的基因组选择中的资源分配方法

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. Show Chinese abstract

Abstract: 基因组选择（GS）是一种植物育种者用来选择个体进行交配并产生新物种世代的技术。 资源分配是GS中的关键因素。 在每次选择周期中，育种者面临着如何分配预算以进行杂交并产生下一代育种亲本的选择。 受最近人工智能问题强化学习进展的启发，我们开发了一种基于强化学习的算法，以自动学习在不同世代之间分配有限资源。 我们通过定义状态空间和动作空间，在马尔可夫决策过程（MDP）的框架下对问题进行了数学公式化。 为了避免状态空间的爆炸，提出了一种整数线性规划方法，该方法量化了资源与时间之间的权衡。 最后，我们提出了一种价值函数近似方法来估计动作价值函数，然后开发了一种贪心策略改进技术以找到最优资源分配。 我们通过一个使用现实数据的案例研究，展示了所提出方法在提高遗传增益方面的有效性。