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When human does a decision-making, h/she finally does it using the various functions in the brain. He/she also has the ability to learn to improve the decision and get better results than before. Reinforcement learning, one of machine learning methods, is mimicking of learning function of the biological brain's basal ganglia. In this study, we propose a novel method that combines the conventional...
Robust control theory generally guarantees robustness and stability of the closed-loop system, however it requires mathematical model of the system to design the control system. Therefore, it can't often deal with nonlinear systems because of difficulty of modeling of the system. Other, reinforcement learning method can deal with the nonlinear system without mathematical model, however, it usually...
A hybrid intelligent control system model which combines high-level time Petri net (HLTPN) and Reinforcement Learning (RL) is proposed. In this model, the control system is modeled by HLTPN and system state last time is presented as transitions delay time. For optimizing the transition delay time through learning, a value item is appended to delay time of transition for recording the reward from environment...
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