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arXiv Machine Learning By Andreas Schwung, Steve Yuwono, Sofiene Lassoued, Dorothea Schwung

Distributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models

Read the original on arXiv Machine Learning →

The paper introduces a data‑driven self‑learning control method for highly flexible, modular manufacturing systems. It uses a model‑based reinforcement learning framework that incorporates approximate inverse process models, separating actuation dynamics from state‑space dynamics so that training occurs only in task space. A lightweight feedforward architecture for these inverse models is integrated into standard RL policy networks and tested on a laboratory modular production testbed, showing improved performance and faster training, especially for off‑policy algorithms.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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