STOCHASTlC POLlCY IN Q-LEARNING USED FOR CONTROL OF AMB

Abstract cze:
Abstrakt: A great intention is lately focused on Reinforcement Learning (RL) methods. The article is focused on improving model free RL method known as Q-Iearning algorithm used on active magnetic bearing (AMB) model. Stochastic strategy and adaptive integration step increased the speed of learning approximately hundred times. Impossibility of using proposed improvement online is the only drawback, however it might be used for pretraining on simulation model and further fined online.

Abstract eng:
Abstract: A great intention is lately focused on Reinforcement Learning (RL) methods. The article is focused on improving model free RL method known as Q-Iearning algorithm used on active magnetic bearing (AMB) model. Stochastic strategy and adaptive integration step increased the speed of learning approximately hundred times. Impossibility of using proposed improvement online is the only drawback, however it might be used for pretraining on simulation model and further fined online.

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