Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement

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.