SLM, Unity, environment
npm install slm-env-unityreacher-v0@0.0.1



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-unitywalker-v0.


Building a binary requires 3 things:

  1. the Unity editor, installed via Unity Hub. Go to Unity Hub > Installs > Editor > Add Modules > Linux Build Support to enable Linux builds.
  2. ml-agents repo with the environment's Unity assets:
git clone
  1. this repo:
git clone

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.

  1. Open the ml-agents/UnitySDK folder in the Unity editor.

  2. In the Assets tab, find Walker under ML-Agents > Examples > Walker > Scenes > Walker. Hit the play button to preview it.

  3. Make any necessary asset changes:

  4. to enable programmatic control, go to WalkerAcademy and check control in the Inspector tab.

  5. open the asset Walker > Brains > WalkerLearning and in the Inspector tab, change Vector Observation > Stacked Vectors to 1. Also, click on Model and delete it so we don't include the pretrained TF weights.

  6. Go to Window > Rendering > LightingSettings and uncheck Realtime Global Illumination and Baked Global Illumination. This is to prevent Enlighten from being used and spawning too many threads on Linux.

  7. Now we're ready to build the binaries. Go to File > Build Settings:

  8. click Add Open Scenes and add your scene

  9. click Player Settings to show the Inspector tab. Check Run in Background, set Display Resolution Dialog to 'Disabled'. Optionally, set Fullscreen Mode to 'Windowed'.

  10. build one for Mac OS X. Hit Build and Run to render immediately after building. Choose the directory SLM-Env/build/ and use the name UnityWalker-v0.

  11. build one for Linux. Hit Build, and use the same directory and name.

  12. Test the binary. First ensure you have the gym_unity pip packages installed from ml-agents. Use the following script to run an example control loop:

from gym_unity.envs import UnityEnv

env = UnityEnv('/Users/YOURNAME/SLM-Env/build/UnityWalker-v0', 0, multiagent=True)

state = env.reset()
for i in range(500):
    action = env.action_space.sample()
    state, reward, done, info = env.step(action)


  • git commit the binaries in build/, then push it.
  • clone this repo under SLM Lab: git clone ./slm_lab/env/SLM-Env