Adapting world models to manufacturing-style decision problems — looking for feedback

I’m exploring whether “world model” ideas from RL can be adapted to manufacturing-style decision problems — an Industrial World Model for Manufacturing.

I put together a small open-source synthetic benchmark around process-window recommendation. The idea is to model a manufacturing process as a state-transition problem under constraints, sparse observations, uncertainty, and next-experiment decisions.

The current repo includes a runnable toy environment, simple baseline planners, uncertainty-aware recommendation logic, and an example visualization. It is not a production model and does not include proprietary data — it is meant as a lightweight public scaffold for discussing manufacturing-style decision problems in RL/world-model terms.

I’d especially appreciate feedback or contributions on:

• whether the state/action/reward framing is technically reasonable
• what baseline planners should be added first
• what decision-quality metrics would make sense
• how to represent physical constraints and sparse/noisy observations
• whether this kind of benchmark could be useful for RL/world-model researchers interested in physical systems

Ways to help:

• try the demo or release
• open an issue with technical feedback
• submit a small PR for a baseline, metric, or environment improvement
• suggest related RL/world-model papers or repos I should compare against
• share it with anyone interested in world models for physical/industrial decision-making

Repo: https://github.com/programmablemanufacturing/programmable-manufacturing-lab