|
---
|
|
library_name: ml-agents
|
|
tags:
|
|
- SoccerTwos
|
|
- deep-reinforcement-learning
|
|
- reinforcement-learning
|
|
- ML-Agents-SoccerTwos
|
|
---
|
|
|
|
# **poca** Agent playing **SoccerTwos**
|
|
This is a trained model of a **poca** agent playing **SoccerTwos**
|
|
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-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: oookayamaswallow/poca-SoccerTwos
|
|
3. Step 2: Select your *.nn /*.onnx file
|
|
4. Click on Watch the agent play 👀
|
|
|
