Q-Learning, SARSA and DQN (Deep Q-Network) Reinforcement Learning based mobile robot navigation

This repository contains codes to run a Reinforcement Learning based navigation.

There are three algorithms provided which are Q-Learning, SARSA, and DQN. A Turtlebot3 mobile robot platform was used to train and test these algorithms.

If you have found this repository useful or have used this repository in any of your scientific work, please consider citing my work using this BibTeX Citation. A full demonstration video of the mobile robot navigation has been uploaded on Youtube.

Table of contents

• Installation
• Repository contents
• Getting started
• Hardware and software information
• BibTeX Citation
• Acknowledgments

Installation

• Firstly, the following packages (openai_ros, turtlebot3) and their dependencies should be cloned in your ROS workspace.
• Then, clone this repository and move the contents openai_ros and turtlebot3 to the installed packages.
• Finally, the ROS workspace should be compiled with catkin_make and sourced with source devel/setup.bash. The compile process should return no error if all the dependencies are met.

Repository contents

my_turtlebot3_training - This folder contains files for the robot to run Deep Q-Network, Q-Learning, and Sarsa algorithm for training and testing.

openai_projects - This folder ontains the RL trained models and logged data during training and testing.

openai_ros - This folder contains files for setting up the RL environment.

turtlebot3 - This folder contains the Gazebo simulation launch files, models, and worlds.

turtlebot3_rl - This folder contains the codes for deploying Deep Q-Network, Q-Learning, and Sarsa algorithm in the real environment with the physical Turtlebot3.

Getting Started

Simulation training

1. Run roslaunch my_turtlebot3_training start_dqlearn_training.launch in your terminal.

Simulation testing

2. Run roslaunch my_turtlebot3_training start_dqlearn_testing.launch in your terminal.

Real world testing (deployment)

3. Physical deployment requires the Turtlebot3 itself and a remote PC to run.

4. On the Turtlebot3:

• Run roslaunch turtlebot3_bringup turtlebot3_robot.launch

5. On the remote PC:

• Run roscore
• Run roslaunch turtlebot3_bringup turtlebot3_remote.launch
• Run roslaunch turtlebot3_rl deploy_turtlebot3.launch

Hardware and Software Information

Software

• OS: Ubuntu 18.04
• ROS version: Melodic
• Python version: 2.7
• Gazebo version: 9
• CUDA version: 10.0
• CuDNN version: 7

Computer Specifications

• CPU: Intel i7 9700
• GPU: Nvidia RTX 2070

Mobile Robot Platform

• Turtlebot3

BibTeX Citation

If you have used this repository in any of your scientific work, please consider citing my work:

@inproceedings{anas2022comparison,
  title={Comparison of Deep Q-Learning, Q-Learning and SARSA Reinforced Learning for Robot Local Navigation},
  author={Anas, Hafiq and Ong, Wee Hong and Malik, Owais Ahmed},
  booktitle={International Conference on Robot Intelligence Technology and Applications},
  pages={443--454},
  year={2022},
  organization={Springer}
}

Acknowledgments

• Thank you Robolab@UBD for lending the Turtlebot3 robot platform and lab facilities.