# Asynchronous Advantage Actor-Critic Agent for Starcraft II

arXiv: https://arxiv.org/abs/1807.08217 · HTML: https://mlsanity.com/paper/1807.08217/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2018.

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

- [Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI](https://mlsanity.com/paper/2508.21101/) (2025)
- [Adversary agent reinforcement learning for pursuit-evasion](https://mlsanity.com/paper/2108.11010/) (2021)

## Abstract (arXiv)

Deep reinforcement learning, and especially the Asynchronous Advantage Actor-Critic algorithm, has been successfully used to achieve super-human performance in a variety of video games. Starcraft II is a new challenge for the reinforcement learning community with the release of pysc2 learning environment proposed by Google Deepmind and Blizzard Entertainment. Despite being a target for several AI developers, few have achieved human level performance. In this project we explain the complexities of this environment and discuss the results from our experiments on the environment. We have compared various architectures and have proved that transfer learning can be an effective paradigm in reinforcement learning research for complex scenarios requiring skill transfer.