000150589 001__ 150589
000150589 005__ 20260113075014.0
000150589 0247_ $$2doi$$a10.1007/s00521-024-10540-4
000150589 0248_ $$2sideral$$a142609
000150589 037__ $$aART-2024-142609
000150589 041__ $$aeng
000150589 100__ $$aVelez Bedoya, Jairo Ivan
000150589 245__ $$aIntegration of causal inference in the DQN sampling process for classical control problems
000150589 260__ $$c2024
000150589 5203_ $$aIn this study, causal inference is integrated into deep reinforcement learning to enhance sampling in classical control environments. The problem we’re working on is "classical control," where an agent makes decisions to keep systems balanced. With the help of artificial intelligence and causal inference, we have developed a method that adjusts a deep Q-network’s experience memory by adjusting the priority of transitions. According to the agent’s actions, these priorities are based on the magnitude of causal differences. We have applied our methodology to a reference environment in reinforcement learning. In comparison with a deep Q-network based on conventional random sampling, the results indicate significant improvements in performance and learning efficiency. Our study shows that causal inference can be integrated into the sampling process so that experience transitions can be selected more intelligently, resulting in more effective learning for classical control problems. The study contributes to the convergence between artificial intelligence and causal inference, offering new perspectives for the application of reinforcement learning techniques in real-world applications where precise control is essential.
000150589 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000150589 592__ $$a1.102$$b2024
000150589 593__ $$aSoftware$$c2024$$dQ1
000150589 593__ $$aArtificial Intelligence$$c2024$$dQ1
000150589 594__ $$a11.7$$b2024
000150589 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000150589 700__ $$0(orcid)0000-0002-8263-2444$$aGonzalez Bedia, Manuel$$uUniversidad de Zaragoza
000150589 700__ $$aCastillo Ossa, Luis Fernando
000150589 700__ $$aArango Lopez, Jeferson
000150589 700__ $$aMoreira, Fernando
000150589 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000150589 773__ $$g(2024), [13 pp.]$$pNeural comput. appl.$$tNeural Computing and Applications$$x0941-0643
000150589 8564_ $$s1810528$$uhttps://zaguan.unizar.es/record/150589/files/texto_completo.pdf$$yVersión publicada$$zinfo:eu-repo/date/embargoEnd/2025-11-29
000150589 8564_ $$s1746427$$uhttps://zaguan.unizar.es/record/150589/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada$$zinfo:eu-repo/date/embargoEnd/2025-11-29
000150589 909CO $$ooai:zaguan.unizar.es:150589$$particulos$$pdriver
000150589 951__ $$a2026-01-12-13:24:19
000150589 980__ $$aARTICLE