ppo Agent playing Huggy
This is a trained model of a ppo agent playing Huggy using the Unity ML-Agents Library.
Training Results
- Training steps: 2,000,000
- Final mean reward: ~3.76 (std 2.01), peaked around 3.95 mid-training
- Training time: ~45 minutes on a Colab GPU
- Trained as part of the Hugging Face Deep RL Course, Bonus Unit 1
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: asiful2/ppo-Huggy
- Step 2: Select your .nn /.onnx file
- Click on Watch the agent play 👀
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