Crop breeding is crucial in improving agricultural productivity while
potentially decreasing land usage, greenhouse gas emissions, and water
consumption. However, breeding programs are challenging due to long turnover
times, high-dimensional decision spaces, long-term objectives, and the need to
adapt to rapid climate change. This paper introduces the use of Reinforcement
Learning (RL) to optimize simulated crop breeding programs. RL agents are
trained to make optimal crop selection and cross-breeding decisions based on
genetic information. To benchmark RL-based breeding algorithms, we introduce a
suite of Gym environments. The study demonstrates the superiority of RL
techniques over standard practices in terms of genetic gain when simulated in
silico using real-world genomic maize data.

利用强化学习技术对模拟的作物育种计划进行优化，以此来改善农业生产力并减少土地使用、温室气体排放和水消耗。使用真实基因组玉米数据对基于强化学习的育种算法与标准育种方法进行对比，研究表明强化学习技术在遗传增益方面具有优势。

强化学习优化育种计划

Breeding Programs Optimization with Reinforcement Learning

Genomic selection (GS), as a critical crop breeding strategy, plays a key
role in enhancing food production and addressing the global hunger crisis. The
predominant approaches in GS currently revolve around employing statistical
methods for prediction. However, statistical methods often come with two main
limitations: strong statistical priors and linear assumptions. A recent trend
is to capture the non-linear relationships between markers by deep learning.
However, as crop datasets are commonly long sequences with limited samples, the
robustness of deep learning models, especially Transformers, remains a
challenge. In this work, to unleash the unexplored potential of attention
mechanism for the task of interest, we propose a simple yet effective
Transformer-based framework that enables end-to-end training of the whole
sequence. Via experiments on rice3k and wheat3k datasets, we show that, with
simple tricks such as k-mer tokenization and random masking, Transformer can
achieve overall superior performance against seminal methods on GS tasks of
interest.

通过使用简单而有效的基于 Transformer 的框架，在 rice3k 和 wheat3k 数据集上进行实验，通过简单的技巧如 k-mer 标记和随机掩蔽，Transformer 可以在感兴趣的 GS 任务中表现出整体优越性能，从而释放关注机制的未开发潜力。