# An Introduction of mini-AlphaStar

arXiv: https://arxiv.org/abs/2104.06890 · HTML: https://mlsanity.com/paper/2104.06890/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [StarCraft II: A New Challenge for Reinforcement Learning](https://mlsanity.com/paper/1708.04782/) (2017)
- [On Reinforcement Learning for Full-length Game of StarCraft](https://mlsanity.com/paper/1809.09095/) (2018)
- [TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game](https://mlsanity.com/paper/1809.07193/) (2018)
- [Modular Architecture for StarCraft II with Deep Reinforcement Learning](https://mlsanity.com/paper/1811.03555/) (2018)
- [Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks](https://mlsanity.com/paper/1609.02993/) (2016)

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

- [Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach](https://mlsanity.com/paper/2312.11865/) (2023)
- [Neurosymbolic Reinforcement Learning and Planning: A Survey](https://mlsanity.com/paper/2309.01038/) (2023)
- [SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language models](https://mlsanity.com/paper/2401.17749/) (2024)
- [On Efficient Reinforcement Learning for Full-length Game of StarCraft II](https://mlsanity.com/paper/2209.11553/) (2022)
- [SMAC-R1: The Emergence of Intelligence in Decision-Making Tasks](https://mlsanity.com/paper/2410.16024/) (2024)
- [EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making](https://mlsanity.com/paper/2508.09586/) (2025)
- [Adaptive Command: Real-Time Policy Adjustment via Language Models in StarCraft II](https://mlsanity.com/paper/2508.16580/) (2025)
- [TacticCraft: Natural Language-Driven Tactical Adaptation for StarCraft II](https://mlsanity.com/paper/2507.15618/) (2025)
- [Rethinking of AlphaStar](https://mlsanity.com/paper/2108.03452/) (2021)
- [LLMs Are Not Good Strategists, Yet Memory-Enhanced Agency Boosts Reasoning](https://mlsanity.com/paper/2608.12626/) (2026)

## Abstract (arXiv)

StarCraft II (SC2) is a real-time strategy game in which players produce and control multiple units to fight against opponent's units. Due to its difficulties, such as huge state space, various action space, a long time horizon, and imperfect information, SC2 has been a research hotspot in reinforcement learning. Recently, an agent called AlphaStar (AS) has been proposed, which shows good performance, obtaining a high win rate of 99.8% against human players. We implemented a mini-scaled version of it called mini-AlphaStar (mAS) based on AS's paper and pseudocode. The difference between AS and mAS is that we substituted the hyper-parameters of AS with smaller ones for mini-scale training. Codes of mAS are all open-sourced (https://github.com/liuruoze/mini-AlphaStar) for future research.