Department of Computer Science, Texas Tech University, P.O. Box 43104, Lubbock, TX 79409-3104, USA.
Anesth Analg. 2011 Feb;112(2):360-7. doi: 10.1213/ANE.0b013e31820334a7. Epub 2010 Dec 14.
Reinforcement learning (RL) is an intelligent systems technique with a history of success in difficult robotic control problems. Similar machine learning techniques, such as artificial neural networks and fuzzy logic, have been successfully applied to clinical control problems. Although RL presents a mathematically robust method of achieving optimal control in systems challenged with noise, nonlinearity, time delay, and uncertainty, no application of RL in clinical anesthesia has been reported.
强化学习 (RL) 是一种智能系统技术,在困难的机器人控制问题上取得了成功。类似的机器学习技术,如人工神经网络和模糊逻辑,已成功应用于临床控制问题。虽然 RL 为在面临噪声、非线性、时滞和不确定性的系统中实现最优控制提供了一种数学上可靠的方法,但尚未有 RL 在临床麻醉中的应用报告。