# High-Level Strategy Selection under Partial Observability in StarCraft: Brood War

arXiv: https://arxiv.org/abs/1811.08568 · HTML: https://mlsanity.com/paper/1811.08568/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2018.

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

- [End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availability](https://mlsanity.com/paper/2405.03262/) (2024)
- [Constrained Command-Conditioned Reinforcement Learning with Bandit Strategy Selection in Real-Time Strategy Games](https://mlsanity.com/paper/2610.11663/) (2026)

## Abstract (arXiv)

We consider the problem of high-level strategy selection in the adversarial setting of real-time strategy games from a reinforcement learning perspective, where taking an action corresponds to switching to the respective strategy. Here, a good strategy successfully counters the opponent's current and possible future strategies which can only be estimated using partial observations. We investigate whether we can utilize the full game state information during training time (in the form of an auxiliary prediction task) to increase performance. Experiments carried out within a StarCraft: Brood War bot against strong community bots show substantial win rate improvements over a fixed-strategy baseline and encouraging results when learning with the auxiliary task.