https://github.com/nourmorsy/duckietown_reinforcement
Code to train a duckie mobile Robot agent using reinforcement learning approach, employs the Q-learning algorithm for Lane following Problem
https://github.com/nourmorsy/duckietown_reinforcement
duckietown duckietown-environment lane-following python reinforcement-learning ros-noetic
Last synced: 4 months ago
JSON representation
Code to train a duckie mobile Robot agent using reinforcement learning approach, employs the Q-learning algorithm for Lane following Problem
- Host: GitHub
- URL: https://github.com/nourmorsy/duckietown_reinforcement
- Owner: nourmorsy
- Created: 2023-12-15T00:56:54.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-01T16:48:41.000Z (8 months ago)
- Last Synced: 2025-03-03T03:14:53.106Z (4 months ago)
- Topics: duckietown, duckietown-environment, lane-following, python, reinforcement-learning, ros-noetic
- Language: Makefile
- Homepage:
- Size: 29.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# DuckieTown Robot
## Overview and Goal
This project utilizes the Duckietown platform to develop a lane-following autonomous robot using reinforcement learning. Specifically, it employs the Q-learning algorithm, a popular reinforcement learning approach, for navigating a Duckiebot through a Duckietown environment. The project is designed for educational and research applications, providing a scalable platform for understanding complex tasks in perception, decision-making, and autonomous control.
---
### Simulation and Robot
#### Gazebo Environment
The Gazebo simulation environment includes:
- A 3D representation of Duckietown with roads, traffic lights, and various objects.
- An interface for controlling the Duckiebot through predefined actions like "Turn Left," "Turn Right," and "Forward."
#### Duckiebot Robot
The Duckiebot is a custom-built robot equipped with:
- **Camera**: Used for lane detection.
- **Motors**: Controlled via an H-bridge motor controller.
- **Raspberry Pi**: Runs the Q-learning algorithm and communicates with the sensors and actuators.
---
## Usage
### - Setup
1. Clone the repository:
```bash
git clone
```
2. Install the required dependencies:
```bash
sudo apt update
sudo apt install ros-noetic-desktop-full gazebo python3-opencv python3-numpy
```
3. Source the ROS environment and build the workspace:
```bash
source /opt/ros/noetic/setup.bash
catkin_make
source devel/setup.bash
```
### - Running the Project
1. Launch the simulation with one mobile robot:
```bash
roslaunch duckietown_gazebo main.launch
```
2. Run the Q-learning training module:
```bash
roslaunch lane_following start_training.launch #this run start_qlearn_lane_follow.py for tranning the agent which saved A dedicated checkpoint file.
```
---
### Acknowledgments
This project is based on resources and tools from Duckietown and the educational platform The Construct, where we developed and tested the Duckiebot's Q-learning functionality in simulation. Special thanks to [The Construct](https://app.theconstruct.ai) for providing a comprehensive learning environment.