8/15-16/24 : I am curently trying to improve the SpaceInvaders

DQN algorithm to reach a score above 200 within a limited

processing time (1 hr GPU, 3 hrs CPU, max 500000 timesteps).

I am open to hyperparameters/referrals/suggestions! Thanks :)

Just search for electricwapiti/dqn-SpaceInvadersNoFrameskip-v4

ppo Agent playing Huggy

This is a trained model of a ppo agent playing Huggy using the Unity ML-Agents Library.

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