{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:53:16Z","timestamp":1777704796230,"version":"3.51.4"},"reference-count":6,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,1,4]]},"abstract":"<jats:p>This article describes the use of the Deep Deterministic Policy Gradient network, a deep reinforcement learning algorithm, for mobile robot navigation. The neural network structure has as inputs laser range findings, angular and linear velocities of the robot, and position and orientation of the mobile robot with respect to a goal position. The outputs of the network will be the angular and linear velocities used as control signals for the robot. The experiments demonstrated that deep reinforcement learning\u2019s techniques that uses continuous actions, are efficient for decision-making in a mobile robot. Nevertheless, the design of the reward functions constitutes an important issue in the performance of deep reinforcement learning algorithms. In order to show the performance of the Deep Reinforcement Learning algorithm, we have applied successfully the proposed architecture in simulated environments and in experiments with a real robot.<\/jats:p>","DOI":"10.3233\/jifs-191711","type":"journal-article","created":{"date-parts":[[2020,11,24]],"date-time":"2020-11-24T21:31:41Z","timestamp":1606253501000},"page":"349-361","source":"Crossref","is-referenced-by-count":14,"title":["Deep Deterministic Policy Gradient for Navigation of Mobile Robots"],"prefix":"10.1177","volume":"40","author":[{"given":"Junior Costa","family":"de Jesus","sequence":"first","affiliation":[{"name":"Federal University of Rio Grande, Rio Grande, Rio Grande do Sul, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jair Augusto","family":"Bottega","sequence":"additional","affiliation":[{"name":"Federal University of Santa Maria, Santa Maria, Rio Grande do Sul, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco Antonio de Souza Leite","family":"Cuadros","sequence":"additional","affiliation":[{"name":"Federal Institute of Espirito Santo, Serra, Espirito Santo, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel Fernando Tello","family":"Gamarra","sequence":"additional","affiliation":[{"name":"Processing Department of Electricity, Federal University of Santa Maria, Santa Maria, RioGrande do Sul, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"14","key":"10.3233\/JIFS-191711_ref5","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1080\/08839514.2019.1684778","article-title":"Mobile robot navigation using an objectrecognition software with rgbd images and the yolo algorithm","volume":"33","author":"Dos Reis","year":"2019","journal-title":"Applied Artificial Intelligence"},{"key":"10.3233\/JIFS-191711_ref6","unstructured":"Fairchild C. and Harman T.L. , ROS Robotics By Example, Packt Publishing Ltd (2016)."},{"key":"10.3233\/JIFS-191711_ref11","unstructured":"Joseph L. , Mastering ROS for robotics programming, Packt Publishing Ltd (2015)."},{"issue":"7540","key":"10.3233\/JIFS-191711_ref17","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"issue":"63","key":"10.3233\/JIFS-191711_ref20","doi-asserted-by":"crossref","first-page":"121","DOI":"10.4114\/intartif.vol22iss63pp121-134","article-title":"Article users activitygesture recognition on kinect sensor using convolutional neural networks and fastdtw for controlling movements ofa mobile robot","volume":"22","author":"Pfitscher","year":"2019","journal-title":"Inteligencia Artificial"},{"issue":"5","key":"10.3233\/JIFS-191711_ref29","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1103\/PhysRev.36.823","article-title":"On the theory of the brownian motion","volume":"36","author":"Uhlenbeck","year":"1930","journal-title":"Physical Review"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-191711","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:02Z","timestamp":1777455722000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-191711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,4]]},"references-count":6,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-191711","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,4]]}}}