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dc.contributor.authorGuttulsrud, Håkon
dc.contributor.authorSandnes, Mathias
dc.contributor.authorShrestha, Raju
dc.date.accessioned2024-02-29T07:30:19Z
dc.date.available2024-02-29T07:30:19Z
dc.date.created2024-02-28T18:23:15Z
dc.date.issued2024
dc.identifier.isbn979-8-4007-0798-8
dc.identifier.urihttps://hdl.handle.net/11250/3120376
dc.description.abstractEfficient space travel requires intelligent and robust control mechanisms during spacecraft landing scenarios. Developing a control mechanism for a rocket trajectory problem is inherently complex. This paper introduces a novel approach using Deep Q-Learning (DQL) with Short-Term Memory (STM) to address the intrinsic challenges of this task. Unlike traditional Q-Learning methods, our DQL STM agent performs in an environment with uncertainties such as starting position, gravity, and wind in both training and simulation, allowing for enhanced robustness in difficult environmental conditions. This adaptation enables the agent to observe 𝑛�-previous state-action pairs, offering a more accurate estimation of environmental dynamics. Experiments demonstrate that this new approach yields better results under stricter testing conditions compared to previous methods. Moreover, we establish the innovative aspects of our methodology through systematic comparisons with basic Q-Learning, highlighting the merits of the DQL STM agent.en_US
dc.language.isoengen_US
dc.relation.ispartofICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleSolving the Lunar Lander Problem with Multiple Uncertainties using a Deep Q-Learning based Short-Term Memory Agenten_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.cristin2250765
dc.source.pagenumber27-33en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal