Folker, H;
Ritchie, M;
Charlish, A;
Griffiths, H;
(2020)
Sensor Path Planning Using Reinforcement Learning.
In:
2020 IEEE 23rd International Conference on Information Fusion (FUSION).
IEEE: Rustenburg, South Africa, South Africa.
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Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Sensor Path Planning Using Reinforcement Learning |
| Event: | Fusion 2020 |
| Dates: | 06 July 2020 - 17 July 2020 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.23919/FUSION45008.2020.9190242 |
| Publisher version: | https://doi.org/10.23919/FUSION45008.2020.9190242 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10105016 |
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