Bridging the Sim-to-Real Gap in Reinforcement Learning-Based Industrial Dispatching through Execution Semantics
arXiv:2605.29078v1 Announce Type: cross Abstract: Event-driven scheduling policies are increasingly deployed in industrial environments, where decisions are made under asynchronous and partially observed system states. As a result, decision states are not temporally consistent, action admissibility is not explicitly defined, and the origin of execution errors remains ambiguous. These issues limit both reliability and interpretability. To address this gap, a policy-neutral execution and measurement layer is proposed to mediate between scheduling policies and the industrial execution environment
The increasing deployment of AI in industrial automation highlights the practical challenges of deploying simulated policies in real-world, messy environments.
Reliable and interpretable AI in industrial operations is crucial for scaling automation, ensuring safety, and optimizing complex supply chains.
This research proposes a method to bridge the simulation-to-real-world gap for reinforcement learning in industrial dispatching by addressing execution semantics.
- · Industrial automation providers
- · Logistics and supply chain companies
- · AI/ML research labs
- · Manufacturing sector
- · Companies with brittle automation systems
- · Legacy industrial control systems
More robust and deployable AI systems for industrial scheduling and dispatching.
Accelerated adoption of advanced AI in factories and supply chains, leading to increased efficiency and reduced errors.
Higher productivity across industrial sectors and a potential shift in the competitive landscape towards companies leveraging such robust AI.
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Read at arXiv cs.LG