AnyLogic is proud to partner with H2O.ai, the leading automatic machine learning platform. Now, you can use the distinct predictive capabilities of simulation and machine learning together.

To get started with incorporating machine learning (ML) models trained with H2O.ai Driverless AI, we have two documented proof-of-concepts models for download. Embedding ML models into simulations enables new areas of application: data scientists can test solutions in a risk-free space and simulation modelers can access more data-driven inputs. Read on, learn more, and try it for yourself!

Use the the Pypeline library for local access to a trained machine learning model based on Python.

If a deployed trained ML model is accessible by a Python library, then Pypeline – a custom AnyLogic library – can be used to query it. Pypeline allows you to execute Python scripts with arguments or interactively run Python code from within your AnyLogic models, using a local installation of Python.

Use the ONNX Helper library to easily access a trained machine learning (ML) model saved in the “.onnx” file format. ONNX, short for Open Neural Network Exchange, is a standardized, framework interoperable format for trained ML models.

While a standard Java library exists, it requires relatively advanced technical knowledge to use. The ONNX Helper is a free downloadable add-on library for any edition of AnyLogic (Personal Learning Edition, University, or Professional). Through a single object and a single function, it provides a simple and easy way to query ML models.

Use Alpyne, a Python library, for interactively running models exported from the RL experiment.

If an AnyLogic model contains a Reinforcement Learning Experiment and, optionally, some callbacks for triggering an action, then the model can be exported from AnyLogic and controlled in a Python script.