Build Unity environment binaries for SLM-Lab and release on npm for easy distribution.
To use a prebuilt environment, just add its npm package, e.g.
yarn add slm-lab-3dball.
Building a binary requires 4 things:
- Node.js with
- the Unity editor, installed via Unity Hub. Go to
Unity Hub > Installs > Editor > Add Modules > Linux Build Supportto enable Linux builds.
- ml-agents repo with the environment's Unity assets:
git clone https://github.com/Unity-Technologies/ml-agents.git
- this repo:
git clone https://github.com/kengz/SLM-Env.git
Build a Unity Environment binary
The goal is to build MacOSX and Ubuntu binaries that can be used in
ml-agents's gym API. Currently this also means restriction to using only non-vector environments.
In this example, we will use the Walker environment. We also recommend first going through the Unity Hub tutorial to get a basic knowledge about the editor. Reference from here.
ml-agents/UnitySDKfolder in the Unity editor.
In the Assets tab, find Walker under
ML-Agents > Examples > Walker > Scenes > Walker. Hit the play button to preview it.
Make any necessary asset changes:
to enable programmatic control, go to
controlin the Inspector tab.
since we're not supporting vector environments, remove the extra walker clones but selecting all but the first
WalkerPairgame objects unchecking them in the Inspector tab.
next, open the asset
Walker > Brains > WalkerLearningand in the Inspector tab, change
Vector Observation > Stacked Vectorsto 1. Also, click on Model and delete it so we don't include the pretrained TF weights.
Edit > Project Settings > Player > Resolution and Presentation. Ensure
Run in Background (checked) and
Display Resolution Dialog (Disabled).
Now we're ready to build the binaries. Go to
File > Build Settings:
Add Open Scenesand add your scene
Player Settingsto show the Inspector tab. Check
Run in Background, set
Display Resolution Dialogto 'Disabled'. Optionally, set
Fullscreen Modeto 'Windowed'.
build one for Mac OS X. Hit
Build and Runto render immediately after building. Choose the directory
SLM-Env/bin/and use the name
build one for Linux. Hit
Build, and use the same directory and name.
Test the binary. First ensure you have the
gym_unitypip packages installed from ml-agents. Use the following script to run an example control loop:
from gym_unity.envs import UnityEnvenv =state =for i in :action =state, reward, done, info =
The binary is now ready. Next, release it to
Note: use kebab-case naming convention with prefix
slm-envand OpenAI gym convention, so
- Open up
- update version
Copy both the MacOSX and Linux binary files from
npm(make sure you are logged in first, by
Since the binaries are huge,
npm will throw an error near the end of it. Just ignore that.
npm ERR! registry error parsing jsonnpm ERR! publish Failed PUT 403npm ERR! code E403npm ERR! You cannot publish over the previously published version 1.0.0. : slm-env-unitywalker-v0
It should be available on npmjs.com, just search for your package
- Add the release to
yarn add slm-env-3dball