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Temporal Property-driven Design Space Exploration with Reinforcement Learning for Cyber-Physical Systems

US & West ·

A workflow for CPS design-space exploration using reinforcement learning is presented. The RL agent selects subsystem alternatives, and the model is evaluated via simulation with online temporal-property monitors.

Why it matters

Automating exploration of large design spaces for complex systems accelerates development and improves decision quality.

Relevant to DT products

Original headline
Temporal Property-driven Design Space Exploration with Reinforcement Learning for Cyber-Physical Systems
Read the original: arXiv — Systems and Control (eess.SY)

Translation and summary are machine-generated from the source. The full article is not republished; its rights belong to the source.

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