The Hugging Face Integration

The Hugging Face Hub 🤗 is a central place where anyone can share and download models.

It allows you to:

Host your trained models.
Download trained models from the community.
Visualize your agents playing directly on your browser.

You can see the list of ml-agents models here.

We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:

A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser
A more in-depth tutorial

Download a model from the Hub

You can simply download a model from the Hub using mlagents-load-from-hf.

You need to define two parameters:

--repo-id: the name of the Hugging Face repo you want to download.
--local-dir: the path to download the model.

For instance, I want to load the model with model-id "ThomasSimonini/MLAgents-Pyramids" and put it in the downloads directory:

mlagents-load-from-hf --repo-id="ThomasSimonini/MLAgents-Pyramids" --local-dir="./downloads"

Upload a model to the Hub

You can simply upload a model to the Hub using mlagents-push-to-hf

You need to define four parameters:

--run-id: the name of the training run id.
--local-dir: where the model was saved
--repo-id: the name of the Hugging Face repo you want to create or update. It’s always <your huggingface username>/<the repo name> If the repo does not exist it will be created automatically
--commit-message: since HF repos are git repositories you need to give a commit message.

For instance, I want to upload my model trained with run-id "SnowballTarget1" to the repo-id: ThomasSimonini/ppo-SnowballTarget:

  mlagents-push-to-hf --run-id="SnowballTarget1" --local-dir="./results/SnowballTarget1" --repo-id="ThomasSimonini/ppo-SnowballTarget" --commit-message="First Push"

Visualize an agent playing

You can watch your agent playing directly in your browser (if the environment is from the ML-Agents official environments)

Step 1: Go to https://huggingface.co/unity and select the environment demo.
Step 2: Find your model_id in the list.
Step 3: Select your .nn /.onnx file.
Step 4: Click on Watch the agent play