交互式框架助力生成艺术参数探索，减少盲目试错。
ParamExplorer: A framework for exploring parameters in generative art
- 基于强化学习设计可交互的参数探索框架
- 支持人工或自动反馈，高效定位美学佳点
- 兼容p5js项目，适合艺术创作者与设计师
生成艺术系统常涉及高维复杂参数空间，其中具有审美吸引力的输出仅占小而分散的区域。由于组合爆炸，艺术家通常依赖大量手动试错，导致许多潜在有趣配置未被发现。本文提出ParamExplorer，一个受强化学习启发的交互式、模块化框架，支持人机协同或自动化反馈，引导生成艺术算法的参数空间探索，并可无缝集成现有p5js项目。在此框架下，我们实现并评估了多种探索策略，称为‘代理’（agents）。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。