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。