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

Abu Bakar, Mohamad Hafiz and Shamsudin, Abu Ubaidah and Abdul Rahim, Ruzairi and Soomro, Zubair Adil 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

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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-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.

Item Type:	Article
Uncontrolled Keywords:	Reinforcement Learning (RL); QLearning; SARSA(State-Action-RewardState-Action); drone
Subjects:	Q Science > QA Mathematics
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	29 Oct 2024 03:07
Last Modified:	29 Oct 2024 03:11
URI:	http://eprints.uthm.edu.my/id/eprint/11650

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