README.md

---
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](https://github.com/Unity-Technologies/ml-agents).

  ## 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
  ```bash
  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](https://huggingface.co/spaces/SamuelM0422/SolarTracker) 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]```.

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