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
- If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
- Step 1: Find your model_id: electricwapiti/ppo-Huggy
- Step 2: Select your .nn /.onnx file
- Click on Watch the agent play 👀