Reinforcement Q-Learning applied to underwater search planning towards maximizing information gain in environments with variable target detection probabilities

Joel Lindsay; Mae L. Seto; Robert Bauer

doi:10.1109/AUV50043.2020.9267901

Reinforcement Q-Learning applied to underwater search planning towards maximizing information gain in environments with variable target detection probabilities

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728187570
DOIs	https://doi.org/10.1109/AUV50043.2020.9267901
Publication status	Published - Sept 30 2020
Event	2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020 - St Johns, Canada Duration: Sept 30 2020 → Oct 2 2020

Publication series

Name	2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020

Conference

Conference	2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020
Country/Territory	Canada
City	St Johns
Period	9/30/20 → 10/2/20

ASJC Scopus Subject Areas

Automotive Engineering
Ocean Engineering
Control and Optimization

Access to Document

10.1109/AUV50043.2020.9267901

Cite this

Lindsay, J., Seto, M. L., & Bauer, R. (2020). Reinforcement Q-Learning applied to underwater search planning towards maximizing information gain in environments with variable target detection probabilities. In 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020 Article 9267901 (2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AUV50043.2020.9267901

Reinforcement Q-Learning applied to underwater search planning towards maximizing information gain in environments with variable target detection probabilities. / Lindsay, Joel; Seto, Mae L.; Bauer, Robert.
2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9267901 (2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lindsay, J, Seto, ML & Bauer, R 2020, Reinforcement Q-Learning applied to underwater search planning towards maximizing information gain in environments with variable target detection probabilities. in 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020., 9267901, 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020, St Johns, Canada, 9/30/20. https://doi.org/10.1109/AUV50043.2020.9267901

Lindsay J, Seto ML , Bauer R. Reinforcement Q-Learning applied to underwater search planning towards maximizing information gain in environments with variable target detection probabilities. In 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9267901. (2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020). doi: 10.1109/AUV50043.2020.9267901

Lindsay, Joel ; Seto, Mae L. ; Bauer, Robert. / Reinforcement Q-Learning applied to underwater search planning towards maximizing information gain in environments with variable target detection probabilities. 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020).

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Reinforcement Q-Learning applied to underwater search planning towards maximizing information gain in environments with variable target detection probabilities

Publication series

Conference

ASJC Scopus Subject Areas

Access to Document

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