README.md

metadata

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. 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

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 👀