Reinforcement learning-based multimodal art element extraction and dynamic adaptation strategy for environmental designs

Artificial intelligence and visual art are enhancing environmental design fast. Traditional artworks in buildings and public places are unresponsive to environmental changes and people’s feelings. By using machine learning, reinforcement learning (RL), multimodal data processing, and optimization, interactive and adaptable art is possible. This paper introduces MVAEx-RL, which extracts and optimizes visual art components. The technology constantly collects data and modifies visual art characteristics based on users and the environment. The system employs deep learning to evaluate color, shape, and texture from images, audio, and sensor readings. Various inputs are fused into a multimodal fusion framework to define the environment. The learning efficacity and adaptability of the RL agent is augmented by Proximal Policy Optimization (PPO), which guarantees stable policy updates and the ability to reach good policies in fewer episodes. The learning framework MVAEx-RL produces more happiness, perceived visual aesthetics, and contextuallyness than static art and RL methods that were not optimized in simulated contexts for creativity. This study instantiates a creativity, intelligent and optimal framework engaging in computational art and new technology to support personalized, adaptable, environmental design.