MOAC-VHP: A Reinforcement Learning Framework for Real-Time Interactive Visual Design - Si, Dongli (Informatica, 2025)
Optimizing interactive solutions in adaptive interface design is crucial as digital platform user interactions and design needs become increasingly complex. Existing systems use static heuristics or rule-based techniques that cannot adapt to real-time user behavior and multi-objective demands. The present research introduces VISO-RL, a reinforcement learning framework that uses a Multi-Objective Actor-Critic with Visual-Attentive Hierarchical Policy (MOAC-VHP) algorithm to improve adaptive interface design. This multidimensional dataset of 8,000 annotated visual samples from the Visual Communication Art Design dataset includes Click-through rates, gaze heatmaps, and scroll depth. These inputs are fed into the system in real-time to adjust design tactics within an adaptive feedback loop. To compare our technique to DRL-CAD, CLIP-RL-UI, and GCN-DRL in a simulated interactive environment that replicates web platform user behavior. Real-Time Multi-objective Alignment (RMA), Visual-Personalization Effectiveness (VPE), and Adaptive Interaction Responsiveness (AIR) were the three main performance criteria used in the experimental evaluation. VISO-RL exceeded baselines in responsiveness, personalization, and multi-objective balance with an AIR score of 3.24, VPE of 4.11, and RMA of 3.97. MOAC-VHP enhanced design responsiveness by 28% and user engagement by 32% compared to rule-based systems. These results demonstrate the model’s ability to create adaptive, personalized, and durable visual interfaces in real-time interactive environments. The architecture combines hierarchical policies and attention, as tested by various experiments. Finally, VISO-RL optimizes adaptive interface design interaction techniques using advanced reinforcement learning.