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
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author Rahman, MD Muhaimin
Rashid, S. M. Hasanur
Hossain, M. M.
author_facet Rahman, MD Muhaimin
Rashid, S. M. Hasanur
Hossain, M. M.
author_sort Rahman, MD Muhaimin
collection PubMed
description 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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spelling pubmed-63028702019-01-04 Implementation of Q learning and deep Q network for controlling a self balancing robot model Rahman, MD Muhaimin Rashid, S. M. Hasanur Hossain, M. M. Robotics Biomim Research 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. Springer Berlin Heidelberg 2018-12-21 2018 /pmc/articles/PMC6302870/ /pubmed/30613463 http://dx.doi.org/10.1186/s40638-018-0091-9 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Research
Rahman, MD Muhaimin
Rashid, S. M. Hasanur
Hossain, M. M.
Implementation of Q learning and deep Q network for controlling a self balancing robot model
title Implementation of Q learning and deep Q network for controlling a self balancing robot model
title_full Implementation of Q learning and deep Q network for controlling a self balancing robot model
title_fullStr Implementation of Q learning and deep Q network for controlling a self balancing robot model
title_full_unstemmed Implementation of Q learning and deep Q network for controlling a self balancing robot model
title_short Implementation of Q learning and deep Q network for controlling a self balancing robot model
title_sort implementation of q learning and deep q network for controlling a self balancing robot model
topic Research
url 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
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