AminVilan/PPO-Huggy

PPO Agent playing Huggy 🐻🤖

A Proximal Policy Optimization (PPO) agent trained to play the Huggy Unity ML-Agents environment, using the Unity ML-Agents Library..
This repository provides pretrained models (.onnx), training configs, evaluation metrics, and can be played in https://huggingface.co/spaces/ThomasSimonini/Huggy.

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Quick Links 🔗

• 📓 Open in Colab
• 💻 GitHub Repo
• 📄 Original PPO Paper

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Usage (with ML-Agents)

The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/

Here is 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: AminVilan/PPO-Huggy
3. Step 2: Select your .nn /.onnx file
4. Click on Watch the agent play 👀

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🙌 If you find this useful, please ⭐ it on Github 🤗