- Docker typically runs a container sharing a (linux) kernel with the host machine, this means that the
Unity environment **has** to be built for the **linux platform**. From the Build Settings Window, please select the architecture to be `x86_64` and choose the build to be `headless` (_This is important because we are running it in a container that does not have graphics drivers installed_).
Save the generated environment in the directory to be mounted (e.g. we have conveniently created an empty directory called at the top level `unity-volume`).
- Ensure that `unity-volume/<environment-name>.x86_64` and `unity-volume/environment-name_Data`. So for example, `<environment_name>` might be `3Dball` and you might want to ensure that `unity-volume/3Dball.x86_64` and `unity-volume/3Dball_Data` are both present in the directory `unity-volume`.
- `source` : Reference to the path in your host OS where you will store the Unity executable.
- `target`: Tells docker to mount the `source` path as a disk with this name.
- `docker-target-name`: Tells the ML-Agents python package what the name of the disk where it can read the Unity executable and store the graph.*This should therefore be identical to the `target`.
- `docker-target-name`: Tells the ML-Agents python package what the name of the disk where it can read the Unity executable and store the graph.**This should therefore be identical to the `target`.**
- `train`, `run-id`: ML-Agents arguments passed to `learn.py`. `train` trains the algorithm, `run-id` is used to tag each experiment with a unique id.
eshvk
