An Introduction of mini-AlphaStar
Ruo-Ze Liu, Wenhai Wang, Yanjie Shen, Zhiqi Li, Yang Yu, Tong Lu

TL;DR
This paper introduces mini-AlphaStar, a scaled-down version of the AlphaStar agent for StarCraft II, demonstrating its implementation and open-sourcing for future reinforcement learning research.
Contribution
The paper presents a mini-scale implementation of AlphaStar with adjusted hyperparameters, providing an accessible version for further research and experimentation.
Findings
Achieved a scaled-down version of AlphaStar for StarCraft II
Open-sourced the code for community use
Facilitates future reinforcement learning research in complex environments
Abstract
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.
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Code & Models
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Taxonomy
TopicsArtificial Intelligence in Games · Reinforcement Learning in Robotics · Evolutionary Algorithms and Applications
MethodsBitcoin Customer Service Number +1-833-534-1729 · Multi-Head Attention · Attention Is All You Need · Linear Layer · Mixing Adam and SGD · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Batch Normalization · 1x1 Convolution · Kaiming Initialization
