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Deep reinforcement learning for quadrotor path following with adaptive velocity

Author
Rubi, B.; Morcego, B.; Perez, R.
Type of activity
Journal article
Journal
Autonomous robots
Date of publication
2020-10-24
Volume
45
First page
119
Last page
145
DOI
10.1007/s10514-020-09951-8
Project funding
Intelligent Supervision for the Predictive Maintenance of Industrial Systems
Safety and control of autonomous vehicles
Repository
http://hdl.handle.net/2117/334076 Open in new window
https://www.researchgate.net/publication/344648971_Deep_Reinforcement_Learning_for_Quadrotor_Path_Following_with_Adaptive_Velocity Open in new window
URL
https://link.springer.com/article/10.1007/s10514-020-09951-8 Open in new window
Abstract
This paper proposes a solution for the path following problem of a quadrotor vehicle based on deep reinforcement learning theory. Three different approaches implementing the Deep Deterministic Policy Gradient algorithm are presented. Each approach emerges as an improved version of the preceding one. The first approach uses only instantaneous information of the path for solving the problem. The second approach includes a structure that allows the agent to anticipate to the curves. The third agent...
Citation
Rubi, B.; Morcego, B.; Perez, R. Deep reinforcement learning for quadrotor path following with adaptive velocity. "Autonomous robots", vol. 45, p. 119-134.
Keywords
Deep deterministic policy gradient, Deep reinforcement learning, Path following, Quadrotor, Trajectory control, Unmanned aerial vehicles
Group of research
CS2AC-UPC - Supervision, Safety and Automatic Control

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