Mobile robot application with hierarchical start position DQN

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dc.authorid0000-0003-0187-4079en_US
dc.authorid0000-0002-9913-5946en_US
dc.contributor.authorErkan, Emre
dc.contributor.authorArseri̇m, Muhammet Ali
dc.date.accessioned2024-03-18T12:31:37Z
dc.date.available2024-03-18T12:31:37Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractAdvances in deep learning significantly affect reinforcement learning, which results in the emergence of Deep RL (DRL). DRL does not need a data set and has the potential beyond the performance of human experts, resulting in significant developments in the field of artificial intelligence. However, because a DRL agent has to interact with the environment a lot while it is trained, it is difficult to be trained directly in the real environment due to the long training time, high cost, and possible material damage. Therefore, most or all of the training of DRL agents for real-world applications is conducted in virtual environments. This study focused on the difficulty in a mobile robot to reach its target by making a path plan in a real-world environment. The Minimalistic Gridworld virtual environment has been used for training the DRL agent, and to our knowledge, we have implemented the first real-world implementation for this environment. A DRL algorithm with higher performance than the classical Deep Q-network algorithm was created with the expanded environment. A mobile robot was designed for use in a real-world application. To match the virtual environment with the real environment, algorithms that can detect the position of the mobile robot and the target, as well as the rotation of the mobile robot, were created. As a result, a DRL-based mobile robot was developed that uses only the top view of the environment and can reach its target regardless of its initial position and rotation.en_US
dc.identifier.citationErkan, E. ve Arserim, M. A. (2022). Mobile robot application with hierarchical start position DQN. Computational Intelligence and Neuroscience, 2022, 4115767.en_US
dc.identifier.doi10.1155/2022/4115767en_US
dc.identifier.endpage21en_US
dc.identifier.issn1687-5265
dc.identifier.pmid36105641en_US
dc.identifier.scopus2-s2.0-85137888044en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.hindawi.com/journals/cin/2022/4115767/
dc.identifier.urihttps://hdl.handle.net/11468/13622
dc.identifier.volume2022en_US
dc.identifier.wosWOS:000874824200012
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorArserim, Muhammet Ali
dc.language.isoenen_US
dc.publisherHindawi Limiteden_US
dc.relation.ispartofComputational Intelligence and Neuroscienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlgorithmsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectHumansen_US
dc.titleMobile robot application with hierarchical start position DQNen_US
dc.typeArticleen_US

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