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.