Learning Unmanned Aerial Vehicle Control for Autonomous Target Following

24 Sep 2017Siyi LiTianbo LiuChi ZhangDit-Yan YeungShaojie Shen

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... (read more)

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