Inference Engine

The ML-Agents Toolkit allows you to use pre-trained neural network models inside your Unity games. This support is possible thanks to the Inference Engine. Inference Engine uses compute shaders to run the neural network within Unity.

Supported devices

Inference Engine supports all Unity runtime platforms.

Scripting Backends : Inference Engine is generally faster with IL2CPP than with Mono for Standalone builds. In the Editor, It is not possible to use Inference Engine with GPU device selected when Editor Graphics Emulation is set to OpenGL(ES) 3.0 or 2.0 emulation. Also, there might be non-fatal build time errors when target platform includes Graphics API that does not support Unity Compute Shaders.

In cases when it is not possible to use compute shaders on the target platform, inference can be performed using CPU or GPUPixel Inference Engine backends.

Using Inference Engine

When using a model, drag the model file into the Model field in the Inspector of the Agent. Select the Inference Device: Compute Shader, Burst or Pixel Shader you want to use for inference.

Note: For most of the models generated with the ML-Agents Toolkit, CPU inference (Burst) will be faster than GPU inference (Compute Shader or Pixel Shader). You should use GPU inference only if you use the ResNet visual encoder or have a large number of agents with visual observations.

Unsupported use cases

Externally trained models

The ML-Agents Toolkit only supports the models created with our trainers. Model loading expects certain conventions for constants and tensor names. While it is possible to construct a model that follows these conventions, we don't provide any additional help for this. More details can be found in TensorNames.cs and SentisModelParamLoader.cs.

If you wish to run inference on an externally trained model, you should use Inference Engine directly, instead of trying to run it through ML-Agents.

Model inference outside of Unity

We do not provide support for inference anywhere outside of Unity. The .onnx files produced by training use the open format ONNX; if you wish to convert a .onnx file to another format or run inference with them, refer to their documentation.