Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning

Authors

  • Mohamad Hafiz Abu Bakar Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
  • Abu Ubaidah bin Shamsudin Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
  • Ruzairi Abdul Rahim Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
  • Zubair Adil Soomro Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia
  • Andi Adrianshah Universitas Mercu Buana Jakarta, Jl. Raya, RT.4/RW.1, Meruya Sel., Kec. Kembangan, Jakarta, Daerah Khusus Ibukota Jakarta, 11650, Indonesia

DOI:

https://doi.org/10.37934/araset.30.3.6978

Keywords:

Reinforcement Learning (RL), Q-learning, SARSA(State-Action-Reward-State-Action), drone

Abstract

Nowadays, the advancement of drones is also factored in the development of a world surrounded by technologies. One of the aspects emphasized here is the difficulty of controlling the drone, and the system developed is still under full control by the users as well. Reinforcement Learning is used to enable the system to operate automatically, thus drone will learn the next movement based on the interaction between the agent and the environment. Through this study, Q-Learning and State-Action-Reward-State-Action (SARSA) are used in this study and the comparison of results involving both the performance and effectiveness of the system based on the simulation of both methods can be seen through the analysis. A comparison of both Q-learning and State-Action-Reward-State-Action (SARSA) based systems in autonomous drone application was performed for evaluation in this study. According to this simulation process is shows that Q-Learning is a better performance and effective to train the system to achieve desire compared with SARSA algorithm for drone controller.

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Author Biographies

Mohamad Hafiz Abu Bakar , Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia

apiz.bakar@gmail.com

Abu Ubaidah bin Shamsudin, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia

ubaidah@uthm.edu.my

Ruzairi Abdul Rahim , Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia

ruzairi@fke.utm.my

Zubair Adil Soomro, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Batu Pahat, Johor, Malaysia

ge220030@student.uthm.edu.my

Andi Adrianshah, Universitas Mercu Buana Jakarta, Jl. Raya, RT.4/RW.1, Meruya Sel., Kec. Kembangan, Jakarta, Daerah Khusus Ibukota Jakarta, 11650, Indonesia

andi@mercubuana.ac.id

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Published

2023-05-15

How to Cite

Mohamad Hafiz Abu Bakar, Abu Ubaidah bin Shamsudin, Ruzairi Abdul Rahim, Zubair Adil Soomro, & Andi Adrianshah. (2023). Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning. Journal of Advanced Research in Applied Sciences and Engineering Technology, 30(3), 69–78. https://doi.org/10.37934/araset.30.3.6978

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