REINFORCEMENT LEARNING PADA MOBILE ROBOT OMNIDIRECTIONAL

LUBIS, FIKRI AULIA PARLINDUNGAN and Nurmaini, Siti and Rendyansyah, Rendyansyah (2020) REINFORCEMENT LEARNING PADA MOBILE ROBOT OMNIDIRECTIONAL. Undergraduate thesis, Sriwijaya University.

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Abstract

Mobile robots are gaining increasing attention not only as an experiment to prove "intelligence" in theory but also as an essential component of industrial and commercial automation. Four-wheeled omnidirectional mobile robot with each wheel having independent drive and seven HC-SR04 ultrasonic sensors were used in this study. Batch reinforcement learning method is used in solving the search for optimal control problems in autonomous robots. The Neural Fitted Q Itration (NFQ) algorithm is used to solve the problem of unrealistic learning sample requirements by storing and reusing tuples of previous transitions of the system in each learning iteration. As a result, in an unobstructed environment, 20 episodes of interaction with the real system, with a success rate of 91%. Then, in the corridor barrier environment, 50 episodes of interaction with the real system, with a success rate of 86.4%. And in a complex obstacle environment, 60 episodes of interaction with real systems, with a success rate of 78.8%.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Learning mobile robot, Autonomous learning robot, Batch reinforcement learning
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning
Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions: 09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User: Fikri Aulia Parlindungan Lubis
Date Deposited: 19 Aug 2020 03:59
Last Modified: 18 Nov 2021 03:51
URI: http://repository.unsri.ac.id/id/eprint/33343

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