Over the past few years, Reinforcement Learning combined with Deep Learning techniques has successfully proven to solve complex problems in various domains including robotics, self-driving cars, finance, and gaming. In this paper, we are introducing Reinforcement Learning (RL) to another domain - visualization. Our novel point-feature label placement method utilizes Multi-Agent Deep Reinforcement Learning (MADRL) to learn 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 the RL learning paradigm, we developed an environment where an agent acts as a proxy for a label, a short textual annotation that augments visualizations like geographical maps, illustrations, and technical drawings. Our results demonstrate that the strategy trained by our method significantly outperforms the random strategy of an untrained agent and also performs superior to 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 compared methods. Nevertheless, our method is ideal for situations 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：过去几年来,强化学习与深学习技术相结合,成功地解决了各种领域的复杂问题,包括机器人、自行驾驶汽车、金融、赌博等领域的复杂问题。在本文件中,我们正在将强化学习(RL)引入另一个领域,即视觉化。我们新颖的点文特色标签定位方法使用多代理深度强化学习(MADRL)学习标签定位战略,这是第一个机械学习驱动的标签方法,与人类专家设计的现有手工设计算法相比。为了便利RL学习模式,我们开发了一种环境,使代理作为标签的代理,一个简短的文字说明,将强化地理图、插图和技术图等的可视化。我们的成果表明,我们所培训的方法大大超越了未经培训的代理商的随机策略,也优于人类专家设计的完整性(即,已安装的标签的数量)方法。为了便利RLL学习模式,我们开发了一个环境,使拟议的方法比标准方法要慢一些。然而,我们在地图、插图和技术图中所使用的方法是理想的,我们用来对用户进行精确性评估的方法,从而显示,在地图上,我们做了更精确地评估。</s>