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dc.contributor.authorGjærum, Vilde Benoni
dc.contributor.authorRørvik, Ella-Lovise H.
dc.contributor.authorLekkas, Anastasios M.
dc.date.accessioned2022-01-24T08:37:27Z
dc.date.available2022-01-24T08:37:27Z
dc.date.created2021-09-30T10:58:40Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/11250/2838850
dc.description.abstractDeep reinforcement learning has led to numerous notable results in robotics. However, deep neural networks (DNNs) are unintuitive, which makes it difficult to understand their predictions and strongly limits their potential for real-world applications due to economic, safety, and assurance reasons. To remedy this problem, a number of explainable AI methods have been presented, such as SHAP and LIME, but these can be either be too costly to be used in real-time robotic applications or provide only local explanations. In this paper, the main contribution is the use of a linear model tree (LMT) to approximate a DNN policy, originally trained via proximal policy optimization(PPO), for an autonomous surface vehicle with five control inputs performing a docking operation. The two main benefits of the proposed approach are: a) LMTs are transparent which makes it possible to associate directly the outputs (control actions, in our case) with specific values of the input features, b) LMTs are computationally efficient and can provide information in real-time. In our simulations, the opaque DNN policy controls the vehicle and the LMT runs in parallel to provide explanations in the form of feature attributions. Our results indicate that LMTs can be a useful component within digital assurance frameworks for autonomous ships.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.titleApproximating a deep reinforcement learning docking agent using linear model treesen_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.journalIEEE Xplore digital libraryen_US
dc.identifier.doi10.23919/ECC54610.2021.9655007
dc.identifier.cristin1941196
dc.description.localcode© IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode0


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