README.md
| 1 | --- |
| 2 | library_name: ml-agents |
| 3 | tags: |
| 4 | - Huggy |
| 5 | - deep-reinforcement-learning |
| 6 | - reinforcement-learning |
| 7 | - ML-Agents-Huggy |
| 8 | --- |
| 9 | |
| 10 | # **ppo** Agent playing **Huggy** |
| 11 | This is a trained model of a **ppo** agent playing **Huggy** |
| 12 | using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). |
| 13 | |
| 14 | ## Usage (with ML-Agents) |
| 15 | The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ |
| 16 | |
| 17 | We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: |
| 18 | - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your |
| 19 | browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction |
| 20 | - A *longer tutorial* to understand how works ML-Agents: |
| 21 | https://huggingface.co/learn/deep-rl-course/unit5/introduction |
| 22 | |
| 23 | ### Resume the training |
| 24 | ```bash |
| 25 | mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume |
| 26 | ``` |
| 27 | |
| 28 | ### Watch your Agent play |
| 29 | You can watch your agent **playing directly in your browser** |
| 30 | |
| 31 | 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity |
| 32 | 2. Step 1: Find your model_id: PaulVialard/ppo-Huggy |
| 33 | 3. Step 2: Select your *.nn /*.onnx file |
| 34 | 4. Click on Watch the agent play 👀 |
| 35 | |