Deep reinforcement learning enhances artistic creativity: The case study of program art students integrating computer deep learning

Abstract: During the artistic journey, creators frequently encounter challenges stemming from pressure, resource constraints, and waning inspiration, all of which can impede their creative flow. Addressing these obstacles requires a multifaceted strategy aimed at nurturing creativity throughout the artistic process. Procedural art generation emerges as a viable solution to invigorate artistic creativity. In this study, the deep Q-network (DQN) was constructed to solve the shortage of artistic creativity through its automatic decision-making ability. The model was trained with different types of artistic styles (abstract and minimalism) in WikiArt dataset. The model generates various artistic elements of different styles, forms, or thinking according to the input parameters or constraints, and selects specific colors, textures, or shapes to help the artist maintain focus in the creation process and expand the creativity in the creation process. In order to achieve this goal, in the process of performing the procedural art generation task with DQN, the experiment collected the generation speed, interpretability, and creativity evaluation feedback of each style of art. The feedback results show that the scores of color field painting and minimalism were 83.2, 93.5, 86.3 and 86.6, 91.5, 82.1 respectively. The research shows that employing dynamic mass spectrometry networks enables the automation of the art creation process. This innovative approach facilitates the exploration of diverse creative ideas tailored to various artistic tasks, thereby fostering advancements in art creation and nurturing creativity.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Deep reinforcement learning enhances artistic creativity: The case study of program art students integrating computer deep learning ; volume:33 ; number:1 ; year:2024 ; extent:19 ; Journal of intelligent systems ; 33, Heft 1 (2024) (gesamt 19)

Beteiligte, Orts- und Zeitangaben

• Urheber: Zhao, Feng

Weitere Informationen

• DOI: 10.1515/jisys-2023-0292
• URN: urn:nbn:de:101:1-2406291548545.064503270150
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 14.08.2025, 10:54 MESZ