Nonblocking Modular Supervisory Control of Discrete Event Systems via Reinforcement Learning and K-Means Clustering

Nonblocking Modular Supervisory Control of Discrete Event Systems via Reinforcement Learning and K-Means Clustering

Traditional supervisory control methods for the nonblocking control of discrete event systems often suffer from exponential computational complexity. Reinforcement learning-based approaches mitigate state explosion by sampling many random sequences instead of computing the synchronous product of mul...

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Bibliographic Details
Main Authors: Junjun Yang, Kaige Tan, Lei Feng
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Machines
Subjects:
discrete event system
supervisory control theory
reinforcement learning
K-means clustering
Online Access:https://www.mdpi.com/2075-1702/13/7/559
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