Implementation of Q learning and deep Q network for controlling a self balancing robot model

Md Muhaimin Rahman; S M Hasanur Rashid; M M Hossain

doi:10.1186/s40638-018-0091-9

Implementation of Q learning and deep Q network for controlling a self balancing robot model

Robotics Biomim. 2018;5(1):8. doi: 10.1186/s40638-018-0091-9. Epub 2018 Dec 21.

Authors

Md Muhaimin Rahman¹, S M Hasanur Rashid¹, M M Hossain²

Affiliations

¹ 1Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
² 2Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.

Abstract

In this paper, the implementations of two reinforcement learnings namely, Q learning and deep Q network (DQN) on the Gazebo model of a self balancing robot have been discussed. The goal of the experiments is to make the robot model learn the best actions for staying balanced in an environment. The more time it can remain within a specified limit, the more reward it accumulates and hence more balanced it is. We did various tests with many hyperparameters and demonstrated the performance curves.