README.md

metadata

library_name: ml-agents
tags:
  - SolarTracker
  - PyTorch
  - deep-reinforcement-learning
  - reinforcement-learning

ppo Agent SolarTracker (SearcherBrain)

This is a trained model of a ppo Solar Tracker to search and track the sun made 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 this Solar Tracker Agent playing

You can watch this agent in action directly in your browser

1. Go to this Hugging Face Space
2. Watch the agent in action! 👀

Input of the model

The action space size is a tensor of 7 elements:

1. The coordinates of the sun in the camera [x, y] normalized
2. A one-hot-encoded vector representing if the sun is visible or not [0 or 1]
3. The quaternion vector representing the rotation of the solar panel [qx, qy, qz, qw].

# input = [x, y, visibility, qx, qy, qz, qw]
example_input = [0.4, 0.5, 1, 0.98, 0, -0.32, -0.99]