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All Journal Journal of Computer Engineering, Electronics and Information Technology
Subhan Hartanto
Universitas Pat Petulai
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

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Analysis of Model-Free Reinforcement Learning Algorithm for Target Tracking Muhammad Fikry; Rizal Tjut Adek; Zulfhazli Zulfhazli; Subhan Hartanto; Taufiqurrahman Taufiqurrahman; Dyah Ika Rinawati
Journal of Computer Engineering, Electronics and Information Technology Vol 1, No 1 (2022): COELITE: Volume 1, Issue 1, 2022
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (574.106 KB) | DOI: 10.17509/coelite.v1i1.43795

Abstract

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.
Co-Authors Dyah Ika Rinawati Muhammad Fikry Rizal S.Si., M.IT, Rizal Taufiqurrahman Taufiqurrahman zulfhazli zulfhazli