Event-Triggered Reinforcement Learning for Optimization of Online Control Systems

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  • イベント駆動型強化学習によるオンライン制御システムの最適化

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<p>In recent years, research on optimization of control systems using online reinforcement learning, which simultaneously learns measures and controls with the measures, has been progressing. We focus on event-driven reinforcement learning as an approach to optimize both control operations and time intervals. Compared with time-driven reinforcement learning, which performs control operations at fixed time intervals, event-driven reinforcement learning can solve the problems of instability caused by unnecessary control operations and increased control cost. However, the performance of event-driven reinforcement learning tends to deteriorate in the early stages of learning due to the effect of initial settings, which is a cause of instability in control using online reinforcement learning.Therefore, we propose a combined time-driven and event-driven reinforcement learning method to improve the performance of event-driven reinforcement learning in the early stages of learning. We also evaluate the performance of the proposed method by conducting computer experiments assuming the control of a heater.</p>

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