| | --- |
| | library_name: ml-agents |
| | tags: |
| | - SnowballTarget |
| | - deep-reinforcement-learning |
| | - reinforcement-learning |
| | - ML-Agents-SnowballTarget |
| | --- |
| | |
| | # **ppo** Agent playing **SnowballTarget** |
| | This is a trained model of a **ppo** agent playing **SnowballTarget** |
| | using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). |
| |
|
| | ## 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 |
| | ```bash |
| | !mlagents-push-to-hf --run-id="SnowballTarget1" --local-dir="./results/SnowballTarget1" --repo-id="salohiddin94/ppo-SnowballTarget" --commit-message="First Push" |
| | ``` |
| |
|
| | ### 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: salohiddin94/ppo-SnowballTarget |
| | 3. Step 2: Select your *.nn /*.onnx file |
| | 4. Click on Watch the agent play 👀 |
| | |
