强化学习用于液体染色学自动化方法开发:对奖励方案和实验预算选择的见解
Leon E Niezen1, Pieter J K Libin2, Deirdre Cabooter3
1Vrije Universiteit Brussel, Department of Chemical Engineering, Pleinlaan 2, 1050 Brussel, Belgium.
Journal of chromatography. A
|March 11, 2025
概括
深度强化学习 (RL) 代理人使用模拟进行训练,以自动化液体染色学 (LC) 方法开发. 邻近政策优化 (PPO) 代理显示出希望,特别是在复杂的梯度程序.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 染色学中的方法开发 (MD) 是复杂的,因为影响化合物化的许多参数.
- 使用强化学习 (RL) 实现液体色谱 (LC) 的自动化MD是一个新兴的领域.
- 深度RL,特别是近距离政策优化 (PPO),正在探索加速LC方法开发.
研究的目的:
- 调查深度强化学习 (RL) 对于自动化液体染色学 (LC) 方法开发的有效性.
- 训练和对比近距离政策优化 (PPO) 代理与贝叶斯优化 (BO) 在寻找最佳色谱条件.
- 在不同复杂度的梯度程序下评估RL代理的性能.
主要方法:
- 使用in-silico方法培训近接政策优化 (PPO) 代理人进行LC方法开发.
- 用稀少的奖励和大量的实验预算训练了代理人,并探索了像堆叠和LSTM这样的策略.
- 训练有素的PPO代理人在1000个随机样本上与贝叶斯优化 (BO) 算法进行了基准测试.
主要成果:
- 对于简单的,单段梯度程序,PPO需要比BO少1-2次实验,但发现的解决方案数量相似 (17%与19%).
- 在具有更多参数的复杂场景中,PPO表现优越,在五段梯度样本中解决了31%,而BO的24%.
- 该研究强调了PPO在应对具有挑战性的LC方法开发任务方面的潜力.
结论:
- 深度强化学习,特别是PPO,是自动化复杂LC方法开发的可行和有效方法.
- 在渐变编程中,PPO在处理更多可调节参数时,比贝叶斯优化具有优势.
- 对RL策略的进一步研究可以显著加快和改进色谱方法优化.
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