Over 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 (MADRL) to learn the label placement strategy, which is the first machine-learning-driven labeling method in contrast to 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 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 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 of the participants.

翻译：近年来，强化学习与深度学习技术的结合已成功解决机器人、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布局领域——这一数据可视化中的复杂任务，旨在寻找最优标签定位以避免重叠并确保可读性。我们提出的新型点要素标签布局方法采用多智能体深度强化学习（MADRL）来学习标签布局策略，这是首个由机器学习驱动的标签方法，区别于现有由人类专家设计的手工算法。为促进强化学习训练，我们构建了一个环境，其中智能体扮演标签代理角色——即增强可视化的短文本注释。结果表明，我们的方法训练出的策略在完整性（即放置标签数量）上显著优于未训练智能体的随机策略和人类专家设计的对比方法。其代价是计算时间增加，使得所提方法速度慢于对比方法。然而，该方法适用于可预先计算标签且完整性至关重要的场景，如制图地图、技术图纸和医学图谱。此外，我们进行了用户研究以评估感知性能。结果显示，参与者认为所提方法显著优于其他测试方法。这表明完整性的提升不仅体现在量化指标上，还反映在参与者的主观评价中。