# SAT-RTS: A systematic framework for tactical knowledge extraction and visualization-based analysis in real-time strategy games

arXiv: https://arxiv.org/abs/2606.30090 · HTML: https://mlsanity.com/paper/2606.30090/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 29 Jun 2026.

## Builds on (most cited first, 10 of 10)

- [StarCraft II: A New Challenge for Reinforcement Learning](https://mlsanity.com/paper/1708.04782/) (2017)
- [Explainable Reinforcement Learning: A Survey](https://mlsanity.com/paper/2005.06247/) (2020)
- [Explainability in Deep Reinforcement Learning](https://mlsanity.com/paper/2008.06693/) (2020)
- [Hierarchical State Abstraction Based on Structural Information Principles](https://mlsanity.com/paper/2304.12000/) (2023)
- [Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning](https://mlsanity.com/paper/2401.12497/) (2024)
- [Combat Models for RTS Games](https://mlsanity.com/paper/1605.05305/) (2016)
- [CODEX: A Cluster-Based Method for Explainable Reinforcement Learning](https://mlsanity.com/paper/2312.04216/) (2023)
- [Mimicking Playstyle by Adapting Parameterized Behavior Trees in RTS Games](https://mlsanity.com/paper/2111.12144/) (2021)
- [Strategy Game-Playing with Size-Constrained State Abstraction](https://mlsanity.com/paper/2408.06202/) (2024)
- [Enhancing Interpretability in Deep Reinforcement Learning through Semantic Clustering](https://mlsanity.com/paper/2409.17411/) (2024)

## Abstract (arXiv)

Efficient tactical knowledge extraction and analysis in real-time strategy (RTS) games micromanagement are constrained by the high-dimensional coupled state-action sequential data and the black-box decision-making process. Current research rarely provides a hierarchical visualization-based attribution analysis from the perspective of data decoupling and abstraction. To facilitate interpretable tactical knowledge extraction and visualization-based analysis in RTS games, a systematic framework named state-action-tactic analysis pipeline (SAT-RTS) is proposed. To decipher the deep-seated drivers of critical decisions in RTS learning systems, this work integrates interpretable visualization with the automated extraction of latent tactical patterns from high-dimensional sequence data. By adapting a cluster-centric BK-tree algorithm and incorporating specialized distance metrics designed to quantify multi-aspect similarities, the proposed framework facilitates robust state-stream abstraction. Furthermore, a rule-based multi-label extraction method is developed to transform unstructured state-action sequences into discrete and interpretable tactical labels, effectively bridging the gap between raw behavioral data and high-level tactical insights. By holistically integrating these computational methods into a hierarchical visualization-based pipeline, the proposed framework effectively addresses the challenges of processing massive real-time data streams while providing fitness landscape visualizations and analytical insights to decipher deep-seated tactical drivers. Comprehensive experiments demonstrate that the proposed SAT-RTS significantly enhances the interpretability and efficiency of tactical analysis in complex RTS environments.