Update README.md

README.md

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----
-library_name: ml-agents
-tags:
-- SearcherBrain
-- deep-reinforcement-learning
-- reinforcement-learning
-- ML-Agents-SearcherBrain
----
-
-  # **ppo** Agent playing **SearcherBrain**
-  This is a trained model of a **ppo** agent playing **SearcherBrain**
-  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 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: SamuelM0422/ppo-SolarTracker
-  3. Step 2: Select your *.nn /*.onnx file
-  4. Click on Watch the agent play 👀
+---
+library_name: ml-agents
+tags:
+- SolarTracker
+- PyTorcj
+- deep-reinforcement-learning
+- reinforcement-learning
+- ML-Agents-SearcherBrain
+---
+
+  # **ppo** Agent playing **SearcherBrain**
+  This is a trained model of a **ppo Solar Tracker** searching and tracking the sun
+  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 your Agent play
+  You can watch this agent in action **directly in your browser**
+
+  1. If the environment is part of ML-Agents official environments, go to [this](https://huggingface.co/spaces/SamuelM0422/SolarTracker) 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 ```bash[x, y]``` normalized
+  2. A one-hot-encoded vector representing if the sun is visible or not ```bash[0 or 1]```
+  3. The quaternion vector representing the rotation of the solar panel.
+
+  ```bash
+  input = [x, y, visibility, qx, qy, qz, qw]
+  ```
+  
+  e.g.
+  ```bash
+  input = [0.4, 0.5, 1, 0.98, 0, -0.32, -0.99]
+  ```