Li, Z;
Chen, WH;
Yang, J;
Yan, Y;
(2023)
AID-RL: Active information-directed reinforcement learning for autonomous source seeking and estimation.
Neurocomputing
, 544
, Article 126281. 10.1016/j.neucom.2023.126281.
(In press).
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Abstract
This paper proposes an active information-directed reinforcement learning (AID-RL) framework for autonomous source seeking and estimation problem. Source seeking requires the search agent to move towards the true source, and source estimation demands the agent to maintain and update its knowledge regarding the source properties such as release rate and source position. These two objectives give rise to the newly developed framework, namely, dual control for exploration and exploitation. In this paper, the greedy RL forms an exploitation search strategy that navigates the agent to the source position, while the information-directed search commands the agent to explore most informative positions to reduce belief uncertainty. Extensive results are presented using a high-fidelity dataset for autonomous search, which validates the effectiveness of the proposed AID-RL and highlights the importance of active exploration in improving sampling efficiency and search performance.
Type: | Article |
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Title: | AID-RL: Active information-directed reinforcement learning for autonomous source seeking and estimation |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1016/j.neucom.2023.126281 |
Publisher version: | https://doi.org/10.1016/j.neucom.2023.126281 |
Language: | English |
Additional information: | © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Autonomous search, Reinforcement learning, Dual control, Active learning, Exploration, Exploitation |
UCL classification: | UCL 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 Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10170728 |
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