ParamExplorer: A framework for exploring parameters in generative art

TL;DR

ParamExplorer is an interactive framework that aids artists in exploring complex parameter spaces in generative art, using reinforcement learning-inspired strategies and feedback to discover aesthetically compelling outputs.

Contribution

The paper introduces ParamExplorer, a novel modular framework for exploring generative art parameters, integrating reinforcement learning concepts and feedback mechanisms.

Findings

Effective exploration strategies implemented and evaluated

Seamless integration with p5js projects demonstrated

Enhanced discovery of interesting generative art configurations

Abstract

Generative art systems often involve high-dimensional and complex parameter spaces in which aesthetically compelling outputs occupy only small, fragmented regions. Because of this combinatorial explosion, artists typically rely on extensive manual trial-and-error, leaving many potentially interesting configurations undiscovered. In this work we make two contributions. First, we introduce ParamExplorer, an interactive and modular framework inspired by reinforcement learning that helps the exploration of parameter spaces in generative art algorithms, guided by human-in-the-loop or even automated feedback. The framework also integrates seamlessly with existing p5js projects. Second, within this framework we implement and evaluate several exploration strategies, referred to as agents.