---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---

# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
For Full Code for this agent, visit: https://www.kaggle.com/code/syedjarullahhisham/drl-huggingface-unit-1-bonus-huggydog

## Codes

Github repos(Give a star if found useful):
  * https://github.com/hishamcse/DRL-Renegades-Game-Bots
  * https://github.com/hishamcse/Advanced-DRL-Renegades-Game-Bots
  * https://github.com/hishamcse/Robo-Chess

Kaggle Notebook:
  * https://www.kaggle.com/code/syedjarullahhisham/drl-huggingface-unit-1-bonus-huggydog


## 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: hishamcse/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀