Bridging the Sim-to-Real Gap in Reinforcement Learning-Based Industrial Dispatching through Execution Semantics
Researchers propose a policy-neutral execution layer that bridges the gap between reinforcement learning scheduling policies and real-world industrial deployment by standardizing decision snapshots, defining explicit action admissibility, and attributing execution failures to specific causes rather than treating them as undifferentiated errors.
Industrial automation increasingly relies on event-driven scheduling policies powered by reinforcement learning, yet deployment in real environments creates persistent reliability challenges. The core problem emerges from asynchronous event streams that lack temporal consistency, unclear action validity constraints, and opaque error attribution when systems fail. This opacity undermines both system trustworthiness and the ability to improve policies through experience.
The proposed execution semantics layer addresses these challenges by introducing a standardized interface between decision-making algorithms and physical systems. By constructing temporally-valid decision snapshots from chaotic event streams and explicitly defining which actions remain admissible in each state, the framework transforms ambiguous failures into categorized, attributable outcomes. The layer distinguishes between policy intent, transactional results, actual physical execution, and human interventions—each a distinct failure mode requiring different remediation strategies.
For industrial operators and AI developers, this architecture offers substantial practical value. Simulation results demonstrate that structured execution outcomes enable operators to prevent avoidable errors before system commitment, particularly when observation lag remains low. The framework converts execution uncertainty into supervisory data that feeds back into policy refinement, creating a virtuous cycle for continuous improvement rather than treating failures as opaque black boxes.
The work addresses a genuine gap in reinforcement learning deployment practices where algorithmic sophistication often exceeds operational transparency. By separating decision semantics from execution behavior, the framework makes mismatch observable and debuggable. Future adoption depends on whether industrial environments standardize around such execution layers and whether the overhead remains acceptable in time-critical dispatch scenarios.
- →Proposed execution layer standardizes interfaces between RL scheduling policies and industrial systems, improving deployment reliability.
- →Framework attributes execution failures to specific causes—policy decisions, transactions, physical execution, or human intervention—enabling targeted improvements.
- →Structured outcome tracking transforms execution uncertainty into supervisory data for continuous policy refinement.
- →Simulation results show strongest operational benefits in low-latency scenarios where errors can be prevented before system commitment.
- →Separation of decision semantics from execution behavior increases interpretability and trustworthiness of autonomous industrial systems.