{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:02:29Z","timestamp":1777705349283,"version":"3.51.4"},"reference-count":15,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,2,14]]},"abstract":"<jats:p>The present study focuses on the implementation of Deep Reinforcement Learning (Deep-RL) techniques for a parallel manipulator robot, specifically the Delta Robot, within a simulated setting. We introduced a simulation framework designed to guide the Delta Robot\u2019s end-effector to a designated spatial point accurately. Within this environment, the robotic agent undergoes a learning process grounded in trial and error. It garners positive rewards for successful predictions regarding the next action and faces negative repercussions for inaccuracies. Through this iterative learning mechanism, the robot refines its strategies, thereby establishing improved decision-making rules based on the ever-evolving environment states. Our investigation delved into three distinct Deep-RL algorithms: the Deep Q-Network Algorithm (DQN), the Double Deep Q-Network (DDQN), and the Trust Region Policy Optimization Algorithm (TRPO). All three methodologies were adept at addressing the challenge presented, and a comprehensive discussion of the findings is encapsulated in the subsequent sections of the paper.<\/jats:p>","DOI":"10.3233\/jifs-232795","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T10:42:41Z","timestamp":1704451361000},"page":"4881-4894","source":"Crossref","is-referenced-by-count":2,"title":["Delta robot control by learning systems: Harnessing the power of deep reinforcement learning algorithms"],"prefix":"10.1177","volume":"46","author":[{"given":"Matheus","family":"dos Santos Lima","sequence":"first","affiliation":[{"name":"Federal University of Santa Maria (UFSM), Santa Maria, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor Augusto","family":"Kich","sequence":"additional","affiliation":[{"name":"University of Tsukuba, Tsukuba, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raul","family":"Steinmetz","sequence":"additional","affiliation":[{"name":"Federal University of Santa Maria (UFSM), Santa Maria, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel Fernando","family":"Tello Gamarra","sequence":"additional","affiliation":[{"name":"Federal University of Santa Maria (UFSM), Santa Maria, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"7540","key":"10.3233\/JIFS-232795_ref4","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","journal-title":"Nature"},{"key":"10.3233\/JIFS-232795_ref5","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of go with deep neural networks andtree 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