Technical Note: \cal Q-Learning
Machine Learning
Model-free learning control of neutralization processes using reinforcement learning
Engineering Applications of Artificial Intelligence
Model-free control based on reinforcement learning for a wastewater treatment problem
Applied Soft Computing
Hierarchical solution of Markov decision processes using macro-actions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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A tight and robust yeast fermentation controller is usually difficult to achieve because of the inherent uncertainty, nonlinear, and time-varying characteristics of the yeast fermentation dynamic process. This paper presented an alternative method for yeast fermentation process control by hybrid reinforcement learning algorithm and fuzzy logic. The fuzzy logic was used to adjust the weighting gain of control action adaptively from reinforcement learning. It led to faster tracking and helped to alleviate the overshoot of the controller. The improved multi-step action Q-learning control algorithm was developed and demonstrated through studies on ethanol concentration control of the yeast fermentation process. Experimental results show that the improved multi-step action Q-learning controller has much lower overshoot, faster tracking, shorter transition, and smoother control signal than the advanced PID controller.