Over the recent years, Reinforcement Learning combined with Deep Learning techniques has successfully proven to solve complex problems in various domains, including robotics, self-driving cars, and finance. In this paper, we are introducing Reinforcement Learning (RL) to label placement, a complex task in data visualization that seeks optimal positioning for labels to avoid overlap and ensure legibility. Our novel point-feature label placement method utilizes Multi-Agent Deep Reinforcement Learning to learn the label placement strategy, the first machine-learning-driven labeling method, in contrast to the existing hand-crafted algorithms designed by human experts. To facilitate RL learning, we developed an environment where an agent acts as a proxy for a label, a short textual annotation that augments visualization. Our results show that the strategy trained by our method significantly outperforms the random strategy of an untrained agent and the compared methods designed by human experts in terms of completeness (i.e., the number of placed labels). The trade-off is increased computation time, making the proposed method slower than the compared methods. Nevertheless, our method is ideal for scenarios where the labeling can be computed in advance, and completeness is essential, such as cartographic maps, technical drawings, and medical atlases. Additionally, we conducted a user study to assess the perceived performance. The outcomes revealed that the participants considered the proposed method to be significantly better than the other examined methods. This indicates that the improved completeness is not just reflected in the quantitative metrics but also in the subjective evaluation by the participants.

翻译：近年来，强化学习与深度学习技术的结合已成功证明能解决机器人、自动驾驶和金融等多个领域的复杂问题。本文首次将强化学习引入标签放置这一数据可视化中的复杂任务——旨在为标签寻找最优位置以避免重叠并确保可读性。我们提出的新颖点特征标签放置方法采用多智能体深度强化学习来学习标签放置策略，这是首个基于机器学习的标签方法，区别于现有由人类专家设计的启发式算法。为促进强化学习训练，我们构建了一个智能体充当标签代理（即增强可视化的短文本注释）的环境。结果表明，我们的方法训练得到的策略在完整性（即放置标签数量）上显著优于未训练智能体的随机策略以及人类专家设计的对比方法。其代价是计算时间增加，使本方法速度慢于对比方法。然而，本方法适用于需提前计算标签且完整性至关重要的场景，例如制图地图、工程图纸和医学图谱。此外，我们通过用户研究评估了感知性能。结果显示，参与者认为本方法显著优于其他受检方法，表明完整性的提升不仅体现在量化指标中，也反映在参与者的主观评价上。