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Sensor path planning using reinforcement learning

: Hoffmann, F.; Charlish, A.; Ritchie, M.; Griffiths, H.


International Society of Information Fusion -ISIF-; Institute of Electrical and Electronics Engineers -IEEE-:
FUSION 2020, 23rd International Conference on Information Fusion : Took place 6-9 July 2020 as a virtual conference, Pretoria, South Africa
Piscataway, NJ: IEEE, 2020
ISBN: 978-0-578-64709-8
ISBN: 978-1-7281-6830-2
8 pp.
International Conference on Information Fusion (FUSION) <23, 2020, Online>
Conference Paper
Fraunhofer FKIE ()

Reinforcement learning is the problem of autonomously learning a policy guided only by a reward function. We evaluate the performance of the Proximal Policy Optimization (PPO) reinforcement learning algorithm on a sensor management task and study the influence of several design choices about the network structure and reward function. The chosen sensor management task is optimizing the sensor path to speed up the localization of an emitter using only bearing measurements. Furthermore, we discuss generic advantages and challenges when using reinforcement learning for sensor management.