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

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 e...

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Main Authors: Abu Bakar, Mohamad Hafiz, Shamsudin, Abu Ubaidah, Abdul Rahim, Ruzairi, Adil Soomro, Zubair, Adrianshah, Andi
Format: Article
Language:English
Published: semarak ilmu 2023
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/9642/1/J16168_3519c3c49183a6f808613789cd52277b.pdf
http://eprints.uthm.edu.my/9642/
https://doi.org/10.37934/araset.30.3.6978
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spelling my.uthm.eprints.96422023-08-16T07:10:29Z http://eprints.uthm.edu.my/9642/ Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning Abu Bakar, Mohamad Hafiz Shamsudin, Abu Ubaidah Abdul Rahim, Ruzairi Adil Soomro, Zubair Adrianshah, Andi T Technology (General) 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-StateAction (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-ActionReward-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. semarak ilmu 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9642/1/J16168_3519c3c49183a6f808613789cd52277b.pdf Abu Bakar, Mohamad Hafiz and Shamsudin, Abu Ubaidah and Abdul Rahim, Ruzairi and Adil Soomro, Zubair and Adrianshah, Andi (2023) Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 30 (3). pp. 69-78. ISSN 2462-1943 https://doi.org/10.37934/araset.30.3.6978
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Abu Bakar, Mohamad Hafiz
Shamsudin, Abu Ubaidah
Abdul Rahim, Ruzairi
Adil Soomro, Zubair
Adrianshah, Andi
Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning
description 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-StateAction (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-ActionReward-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.
format Article
author Abu Bakar, Mohamad Hafiz
Shamsudin, Abu Ubaidah
Abdul Rahim, Ruzairi
Adil Soomro, Zubair
Adrianshah, Andi
author_facet Abu Bakar, Mohamad Hafiz
Shamsudin, Abu Ubaidah
Abdul Rahim, Ruzairi
Adil Soomro, Zubair
Adrianshah, Andi
author_sort Abu Bakar, Mohamad Hafiz
title Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning
title_short Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning
title_full Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning
title_fullStr Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning
title_full_unstemmed Comparison Method Q-Learning and SARSA for Simulation of Drone Controller using Reinforcement Learning
title_sort comparison method q-learning and sarsa for simulation of drone controller using reinforcement learning
publisher semarak ilmu
publishDate 2023
url http://eprints.uthm.edu.my/9642/1/J16168_3519c3c49183a6f808613789cd52277b.pdf
http://eprints.uthm.edu.my/9642/
https://doi.org/10.37934/araset.30.3.6978
_version_ 1775624950118350848
score 13.211869

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