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Machine Learning

Example scenes pertaining to a set of related RIDE API capabilities. 

Overview

Explore a scene to learn more about the API.

Results

Barracuda API

This example demonstrates how to examine and run unit tests on various Machine Learning models residing within/outside RIDE backed with Unity Barracuda framework.

Behaviour Comparison

This scene demonstrates how one might analyze side-by-side the performance of two approaches for agent cover behaviour: scripted vs. Machine Learning (ML).

Localization

Demonstrates how to load, display, and switch between different languages

Training TakeCover ML

Rapid training of mlAgent units for modelling cover behaviour by inference. This scene links to an external python application over Unity TCP containing user-defined observations created in an academy for which the unit “brains” undergo training.

The project/effort/work depicted here was or is sponsored by the U.S. Army Research Laboratory (ARL) under contract number W911NF-14-D-0005. Statements and opinions expressed and content included do not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.