# Rethinking of AlphaStar

arXiv: https://arxiv.org/abs/2108.03452 · HTML: https://mlsanity.com/paper/2108.03452/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 194 references that are in the map.
- First posted on arXiv 7 Aug 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [StarCraft II: A New Challenge for Reinforcement Learning](https://mlsanity.com/paper/1708.04782/) (2017)
- [SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II](https://mlsanity.com/paper/2012.13169/) (2020)
- [TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game](https://mlsanity.com/paper/2011.13729/) (2020)
- [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)
- [TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games](https://mlsanity.com/paper/1611.00625/) (2016)
- [Unsupervised Video Object Segmentation for Deep Reinforcement Learning](https://mlsanity.com/paper/1805.07780/) (2018)
- [Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning](https://mlsanity.com/paper/1903.00715/) (2019)
- [Modular Architecture for StarCraft II with Deep Reinforcement Learning](https://mlsanity.com/paper/1811.03555/) (2018)
- [An Introduction of mini-AlphaStar](https://mlsanity.com/paper/2104.06890/) (2021)

## Built on by (most cited first, 1 of 1)

- [On Efficient Reinforcement Learning for Full-length Game of StarCraft II](https://mlsanity.com/paper/2209.11553/) (2022)

## Abstract (arXiv)

We present a different view for AlphaStar (AS), the program achieving Grand-Master level in the game StarCraft II. It is considered big progress for AI research. However, in this paper, we present problems with the AS, some of which are the defects of it, and some of which are important details that are neglected in its article. These problems arise two questions. One is that what can we get from the built of AS? The other is that does the battle between it with humans fair? After the discussion, we present the future research directions for these problems. Our study is based on a reproduction code of the AS, and the codes are available online.