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https://github.com/abdullah2020/deepracer-student-league-track-waypoints-visualization
This project helps visualize the waypoints of the September DeepRacer Student League track.
https://github.com/abdullah2020/deepracer-student-league-track-waypoints-visualization
ai aws deepracer deepracer-student ml ppo rl
Last synced: about 2 months ago
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This project helps visualize the waypoints of the September DeepRacer Student League track.
- Host: GitHub
- URL: https://github.com/abdullah2020/deepracer-student-league-track-waypoints-visualization
- Owner: Abdullah2020
- Created: 2024-09-16T21:15:31.000Z (5 months ago)
- Default Branch: master
- Last Pushed: 2024-09-16T21:47:53.000Z (5 months ago)
- Last Synced: 2024-12-17T18:13:19.536Z (about 2 months ago)
- Topics: ai, aws, deepracer, deepracer-student, ml, ppo, rl
- Language: Jupyter Notebook
- Homepage:
- Size: 345 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
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:
```bash
pip install numpy matplotlib
```
3. Clone the AWS DeepRacer Community Data repository (the code will automatically clone it if it doesn't exist):
```bash
!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:
