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

arXiv: https://arxiv.org/abs/2303.01388 · HTML: https://mlsanity.com/paper/2303.01388/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 68 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 23 of 23)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [DVQA: Understanding Data Visualizations via Question Answering](https://mlsanity.com/paper/1801.08163/) (2018)
- [Learning to Optimize](https://mlsanity.com/paper/1606.01885/) (2016)
- [Virtual to Real Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1704.03952/) (2017)
- [A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization](https://mlsanity.com/paper/2012.00467/) (2020)
- [Learning Visual Importance for Graphic Designs and Data Visualizations](https://mlsanity.com/paper/1708.02660/) (2017)
- [Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline](https://mlsanity.com/paper/1907.13550/) (2019)
- [A practical guide and software for analysing pairwise comparison experiments](https://mlsanity.com/paper/1712.03686/) (2017)
- [Why? Why not? When? Visual Explanations of Agent Behavior in Reinforcement Learning](https://mlsanity.com/paper/2104.02818/) (2021)
- [Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach](https://mlsanity.com/paper/1808.00703/) (2018)
- [Table2Charts: Recommending Charts by Learning Shared Table Representations](https://mlsanity.com/paper/2008.11015/) (2020)
- [DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning](https://mlsanity.com/paper/1909.02982/) (2019)
- [PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning](https://mlsanity.com/paper/2009.00249/) (2020)
- [Color Crafting: Automating the Construction of Designer Quality Color Ramps](https://mlsanity.com/paper/1908.00629/) (2019)
- [External Labeling Techniques: A Taxonomy and Survey](https://mlsanity.com/paper/1902.01454/) (2019)
- [Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning](https://mlsanity.com/paper/2103.02380/) (2021)

## Built on by (most cited first, 2 of 2)

- [Automated Label Placement on Maps via Large Language Models](https://mlsanity.com/paper/2507.22952/) (2025)
- [Constraint-Safe Graph-Context Scoring for Stable Point-Feature Labels Under Text-Width and Accessibility-Inspired Profiles](https://mlsanity.com/paper/2609.19848/) (2026)

## Abstract (arXiv)

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.