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Author Berenji, H.R. ♦ Vengerov, D. ♦ Ametha, J.
Sponsorship IEEE ♦ IEEE Neurla Networks Soc
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2003
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Learning ♦ Remotely operated vehicles ♦ Mobile robots ♦ Unmanned aerial vehicles ♦ Intelligent sensors ♦ Target tracking ♦ Sensor phenomena and characterization ♦ Intelligent systems ♦ Intelligent vehicles ♦ Vehicle dynamics
Abstract In this paper we study the problem of sensor allocation in Unmanned Aerial Vehicles (UAVs). Each UAV uses perception-based rules for generalizing decision strategy across similar states and reinforcement learning for adapting these rules to the uncertain, dynamic environment. A big challenge for reinforcement learning algorithms in this problem is that UAVs need to learn two complementary policies: how to allocate their individual sensors to appearing targets and how to distribute themselves as a team in space to match the density and importance of targets underneath. We address this problem using a co-evolutionary approach, where the policies are learned separately, but they use a common reward function. The applicability of our approach to the UAV domain is verified using a high-fidelity robotic simulator. Based on our results, we believe that the co-evolutionary reinforcement learning approach to reducing dimensionality of the action space presented in this paper is general enough to be applicable to many other multi-objective optimization problems, particularly those that involve a tradeoff between individual optimality and team-level optimality.
Description Author affiliation: Intelligent Inference Syst. Corp., Sunnyvale, CA, USA (Berenji, H.R.; Vengerov, D.; Ametha, J.)
ISBN 0780378105
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2003-05-25
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 433.21 kB
Page Count 6
Starting Page 125
Ending Page 130

Source: IEEE Xplore Digital Library