{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:26:51Z","timestamp":1773512811680,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process. However, real-world robotic applications often need a data-efficient learning process with safety-critical constraints. In this paper, we consider the challenging problem of learning unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire a strategy that combines perception and control, we represent the policy by a convolutional neural network. We develop a hierarchical approach that combines a model-free policy gradient method with a conventional feedback proportional-integral-derivative (PID) controller to enable stable learning without catastrophic failure. The neural network is trained by a combination of supervised learning from raw images and reinforcement learning from games of self-play. We show that the proposed approach can learn a target following policy in a simulator efficiently and the learned behavior can be successfully transferred to the DJI quadrotor platform for real-world UAV control.\u00a0<\/jats:p>","DOI":"10.24963\/ijcai.2018\/685","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:10Z","timestamp":1530755350000},"page":"4936-4942","source":"Crossref","is-referenced-by-count":23,"title":["Learning Unmanned Aerial Vehicle Control for Autonomous Target Following"],"prefix":"10.24963","author":[{"given":"Siyi","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, HKUST"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianbo","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, HKUST"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, HKUST"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dit-Yan","family":"Yeung","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, HKUST"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaojie","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, HKUST"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:55:16Z","timestamp":1530755716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/685"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/685","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}