Implementation of Q learning and deep Q network for controlling a self balancing robot model

In this paper, the implementations of two reinforcement learnings namely, Q learning and deep Q network (DQN) on the Gazebo model of a self balancing robot have been discussed. The goal of the experiments is to make the robot model learn the best actions for staying balanced in an environment. The m...

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Detalles Bibliográficos
Autores principales: Rahman, MD Muhaimin, Rashid, S. M. Hasanur, Hossain, M. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2018
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302870/
https://www.ncbi.nlm.nih.gov/pubmed/30613463
http://dx.doi.org/10.1186/s40638-018-0091-9
Descripción
Sumario:In this paper, the implementations of two reinforcement learnings namely, Q learning and deep Q network (DQN) on the Gazebo model of a self balancing robot have been discussed. The goal of the experiments is to make the robot model learn the best actions for staying balanced in an environment. The more time it can remain within a specified limit, the more reward it accumulates and hence more balanced it is. We did various tests with many hyperparameters and demonstrated the performance curves. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40638-018-0091-9) contains supplementary material, which is available to authorized users.

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