# DashBot: Insight-Driven Dashboard Generation Based on Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2208.01232 · HTML: https://mlsanity.com/paper/2208.01232/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2022.

## Built on by (most cited first, 10 of 10)

- [Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning](https://mlsanity.com/paper/2407.20174/) (2024)
- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution](https://mlsanity.com/paper/2411.05651/) (2024)
- [Reinforcement Learning for Generative AI: A Survey](https://mlsanity.com/paper/2308.14328/) (2023)
- [Agnostic Visual Recommendation Systems: Open Challenges and Future Directions](https://mlsanity.com/paper/2302.00569/) (2023)
- [MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model](https://mlsanity.com/paper/2501.01014/) (2025)
- [DashChat: Interactive Authoring of Performance Dashboard Design Prototypes through Conversation with LLM-Powered Agent](https://mlsanity.com/paper/2504.12865/) (2025)
- [CycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation](https://mlsanity.com/paper/2512.19173/) (2025)
- [Crystalis: Progressive Nucleation and Semantic Annealing for Coordinated Multi-View Visualization Generation](https://mlsanity.com/paper/2607.24766/) (2026)
- [Intent Graph: Navigating the Analytical Reasoning Space for Exploratory Data Analysis](https://mlsanity.com/paper/2610.11025/) (2026)

## Abstract (arXiv)

Analytical dashboards are popular in business intelligence to facilitate insight discovery with multiple charts. However, creating an effective dashboard is highly demanding, which requires users to have adequate data analysis background and be familiar with professional tools, such as Power BI. To create a dashboard, users have to configure charts by selecting data columns and exploring different chart combinations to optimize the communication of insights, which is trial-and-error. Recent research has started to use deep learning methods for dashboard generation to lower the burden of visualization creation. However, such efforts are greatly hindered by the lack of large-scale and high-quality datasets of dashboards. In this work, we propose using deep reinforcement learning to generate analytical dashboards that can use well-established visualization knowledge and the estimation capacity of reinforcement learning. Specifically, we use visualization knowledge to construct a training environment and rewards for agents to explore and imitate human exploration behavior with a well-designed agent network. The usefulness of the deep reinforcement learning model is demonstrated through ablation studies and user studies. In conclusion, our work opens up new opportunities to develop effective ML-based visualization recommenders without beforehand training datasets.