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

• URL: http://arxiv.org/abs/2303.01388v3
• Date: Mon, 18 Sep 2023 10:20:54 GMT
• Title: Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement
• Authors: Petr Bob\'ak, Ladislav \v{C}mol\'ik, Martin \v{C}ad\'ik
• Abstract summary: We introduce Reinforcement Learning (RL) to label placement, a complex task in data visualization. Our novel point-feature label placement method utilizes Multi-Agent Deep Reinforcement Learning to learn the label placement strategy. Our results show that the strategy trained by our method significantly outperforms the random strategy of an untrained agent.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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