ppo-Huggy

ppo Agent playing Huggy

This is a trained model of a ppo agent playing Huggy using the Unity ML-Agents Library.

Training Results

• Training steps: 2,000,000
• Final mean reward: ~3.76 (std 2.01), peaked around 3.95 mid-training
• Training time: ~45 minutes on a Colab GPU
• Trained as part of the Hugging Face Deep RL Course, Bonus Unit 1

Usage (with ML-Agents)

The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ 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: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
• A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction

Resume the training

  mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume

Watch your Agent play

You can watch your agent playing directly in your browser

1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: asiful2/ppo-Huggy
3. Step 2: Select your .nn /.onnx file
4. Click on Watch the agent play 👀