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papersSEP 12 04:00 UTC

Model-based reinforcement learning with inverse models controls modular production systems

A new arXiv paper proposes a framework for data-driven self-learning control of highly flexible, modular manufacturing systems. The approach combines model-based reinforcement learning with approximate inverse process models to improve distributed optimization. The work targets industrial settings where production modules can be reconfigured.

arXiv Industrial automation Inverse process models Model-based reinforcement learning Modular production systems distributed optimization

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGDistributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models ↗SEP 11 04:00 UTC

arXiv cs.AIDistributed Optimization of Modular Production Systems using Model-based Reinforcement Learning with Inverse Models ↗SEP 12 04:00 UTC