# StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search

arXiv: https://arxiv.org/abs/2006.10525 · HTML: https://mlsanity.com/paper/2006.10525/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

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

- [Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction](https://mlsanity.com/paper/2301.10034/) (2023)
- [Code Driven Planning with Domain-Adaptive Critic](https://mlsanity.com/paper/2509.19077/) (2025)

## Abstract (arXiv)

The real-time strategy game of StarCraft II has been posed as a challenge for reinforcement learning by Google's DeepMind. This study examines the use of an agent based on the Monte-Carlo Tree Search algorithm for optimizing the build order in StarCraft II, and discusses how its performance can be improved even further by combining it with a deep reinforcement learning neural network. The experimental results accomplished using Monte-Carlo Tree Search achieves a score similar to a novice human player by only using very limited time and computational resources, which paves the way to achieving scores comparable to those of a human expert by combining it with the use of deep reinforcement learning.