Constrained reinforcement learning for service

This technical brief treats constrained reinforcement learning for service as a decision system: define its state, objective, feasible actions and evidence before comparing algorithms.

Practitioner lens

Separate prediction from action in constrained reinforcement learning for service: identify what is estimated, what is optimized and which business rules must remain binding regardless of model confidence.

First experiment

Write down the objective, constraints, uncertainty set and decision horizon before selecting a solver or model.

This is an original educational brief. It is a starting point for discussion, not a claim that one technique fits every supply chain.