# Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger

arXiv: https://arxiv.org/abs/1812.00054 · HTML: https://mlsanity.com/paper/1812.00054/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [Light-weight probing of unsupervised representations for Reinforcement Learning](https://mlsanity.com/paper/2208.12345/) (2022)
- [A Self-Supervised Auxiliary Loss for Deep RL in Partially Observable Settings](https://mlsanity.com/paper/2104.08492/) (2021)
- [World Models for Policy Refinement in StarCraft II](https://mlsanity.com/paper/2602.14857/) (2026)
- [Esports World Model Dataset](https://mlsanity.com/paper/2610.16166/) (2026)

## Abstract (arXiv)

We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games. We propose to employ encoder-decoder neural networks for this task, and introduce proxy tasks and baselines for evaluation to assess their ability of capturing basic game rules and high-level dynamics. By combining convolutional neural networks and recurrent networks, we exploit spatial and sequential correlations and train well-performing models on a large dataset of human games of StarCraft: Brood War. Finally, we demonstrate the relevance of our models to downstream tasks by applying them for enemy unit prediction in a state-of-the-art, rule-based StarCraft bot. We observe improvements in win rates against several strong community bots.