This project helps visualize the waypoints of the September'24 DeepRacer Student League track. Understanding the track and its waypoints can assist you in drafting an effective reward function for your AWS DeepRacer model.

Overview

The provided code allows you to:

• Clone the AWS DeepRacer community's race data repository.
• Visualize the waypoints, left, and right boundaries of a given track.
• Label waypoints at regular intervals to understand their distribution and use them to improve your model's reward function.

The code is implemented in Track_Waypoints_display.ipynb, a Jupyter Notebook, and it specifically visualizes the waypoints of the "Austin" track from the community data repository.

Prerequisites

Before running the notebook, ensure you have the following installed:

• Python 3.x
• NumPy (pip install numpy)
• Matplotlib (pip install matplotlib)
• Git (to clone the repository)

Installation

1. Clone this repository or download the .ipynb file.

2. Install the required Python libraries by running:

   pip install numpy matplotlib

3. Clone the AWS DeepRacer Community Data repository (the code will automatically clone it if it doesn't exist):

   !git clone https://github.com/aws-deepracer-community/deepracer-race-data.git

Usage

To run the notebook, open it in a Jupyter environment and execute the code cells. The main function, track_display(), visualizes the track waypoints, allowing you to analyze the track layout.

Sample Output

Below is a sample visualization of the Austin track's waypoints: