Analysis OF model-free reinforcement learning algorithm FOR target tracking

Muhammad, Fikry and Rizal, Tjut Adek and Zulfhazli, Zulfhazli and Subhan, Hartanto and Taufiqurrahman, Taufiqurrahman and Dyah, Ika Rinawati (2022) Analysis OF model-free reinforcement learning algorithm FOR target tracking. Journal of Computer Engineering, Electronics and Information Technology, 1 (1): 1. pp. 1-8. ISSN 2829-4157

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Target tracking is a process that can find points in different domains. In tracking, some places contain prizes (positive or negative values) that the agent does not know at first. Therefore, the agent, which is a system, must learn to get the maximum value with various learning rates. Reinforcement learning is a machine learning technique in which agents learn through interaction with the environment using reward functions and probabilistic dynamics to allow agents to explore and learn about the environment through various iterations. Thus, for each action taken, the agent receives a reward from the environment, which determines positive or negative behavior. The agent's goal is to maximize the total reward received during the interaction. In this case, the agent will study three different modules, namely sidewalk, obstacle, and product, using the Q-learning algorithm. Each module will be training with various learning rates and rewards. Q-learning can work effectively with the highest final reward at a learning rate of 0.8 for 500 rounds with an epsilon of 0.9.

Item Type: Article
Uncontrolled Keywords: Target tracking, Machine learning, Reinforcement learning, Q-Learning
Subjects: Computers, Control & Information Theory > Computer Software
Depositing User: Syifa Naufal Qisty
Date Deposited: 21 Mar 2023 07:54
Last Modified: 21 Mar 2023 07:54

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