Genetic Algorithm for Planning and Scheduling Problem -- StarCraft II Build Order case study by Konrad Gmyrek, Michał Antkiewicz, Pawel Myszkowski

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StarCraft strategy classification of a large human versus human game replay dataset by Štefan Krištofík, Matúš Kasáš, Peter Malík

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StarCraft agent strategic training on a large human versus human game replay dataset by Štefan Krištofík, Matúš Kasáš, Štefan Neupauer, Peter Malík

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Improving esports viewing experience through hierarchical scene detection and tracking by Ho-Taek Joo, Sung-Ha Lee, Insik Chung, Kyung-Joong Kim

Subjects: “…StarCraft…”
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Sport elektroniczny i jego medialne interfejsy by Mateusz Felczak

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SA-MARL: Novel Self-Attention-Based Multi-Agent Reinforcement Learning With Stochastic Gradient Descent by Rabbiya Younas, Hafiz Muhammad Raza Ur Rehman, Ingyu Lee, Byung-Won On, Sungwon Yi, Gyu Sang Choi

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Robustness of performance during domain change in an esport: A study of within-expertise transfer. by Joe Thompson, Justin W O'Camb, Robin C A Barrett, Scott Harrison, Mark R Blair

“…Here we examine skill maintenance in StarCraft 2, a video game of skills which undergoes frequent changes due to updates and includes a variety of gameplay options. …”
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Artificial Intelligence in Video Games: Towards a Unified Framework by Firas Safadi, Raphael Fonteneau, Damien Ernst

“…The approach is illustrated using two video games, Raven and StarCraft: Brood War.…”
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TMAC: a Transformer-based partially observable multi-agent communication method by Xuesi Li, Shuai Xue, Ziming He, Haobin Shi

“…At the same time, we performed experimental verification in the surviving and the StarCraft Multi-Agent Challenge (SMAC) environments where agents had limited local observation and could only communicate with neighboring agents. …”
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Trajectory Based Prioritized Double Experience Buffer for Sample-Efficient Policy Optimization by Shengxiang Li, Ou Li, Guangyi Liu, Siyuan Ding, Yijie Bai

“…Reinforcement learning has recently made great progress in various challenging domains such as board game of Go and MOBA game of StarCraft II. Policy gradient based reinforcement learning method has become the mainstream due to its effectiveness and simplicity both in discrete and continuous scenarios. …”
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Assisted-Value Factorization with Latent Interaction in Cooperate Multi-Agent Reinforcement Learning by Zhitong Zhao, Ya Zhang, Siying Wang, Yang Zhou, Ruoning Zhang, Wenyu Chen

“…Experimental results demonstrate the improved training speed and superior performance of the proposed method in both a multi-agent particle environment and the StarCraft Multi-Agent Challenge.…”
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Hierarchical reinforcement learning based on macro actions by Hao Jiang, Gongju Wang, Shengze Li, Jieyuan Zhang, Long Yan, Xinhai Xu

“…Comprehensive tests on the StarCraft II maps Simple64 and AbyssalReefLE demonstrate that the HRL-MA framework exhibits superior performance, achieving higher win rates compared to baseline algorithms. …”
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A Multitask-Based Transfer Framework for Cooperative Multi-Agent Reinforcement Learning by Cheng Hu, Chenxu Wang, Weijun Luo, Chaowen Yang, Liuyu Xiang, Zhaofeng He

“…Experiments conducted in two popular environments, StarCraft II Multi-Agent Challenge and Google Research Football, demonstrate that our method outperforms the baselines, significantly improving the efficiency of team collaboration.…”
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