u/Consistent_Scene3887

Hi everyone — I’m working on an open-source environment for studying sequential decision-making in manufacturing systems.

The current demo is a synthetic process-window benchmark: an agent/planner selects process settings, observes noisy quality outcomes, tracks uncertainty, and recommends the next experiment. The motivation is similar to sparse-data physical systems, where each real experiment is expensive and the goal is not just prediction, but deciding what to try next.

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

I’d appreciate feedback from the RL community on:

• what baseline planners would be useful to include first;
• whether this should be framed closer to contextual bandits, model-based RL, Bayesian optimization, or POMDP-style planning;
• what metrics would make sense beyond reward, such as regret, sample efficiency, uncertainty calibration, or build-to-confidence.

The goal is to create a small public benchmark that others can critique, extend, or use for educational experiments.

I’m working on a small open repo focused on physics-informed AI for manufacturing.

The goal is not to release a production model, but to create lightweight templates for deciding whether a manufacturing workflow is actually AI-ready: clear inputs/outputs, controllable variables, feedback loops, sparse-data constraints, and where physics priors may help.

Would appreciate feedback from people working on ML for physical systems, scientific ML, or industrial AI.

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