Improvements and Challenges in StarCraft II Macro-Management A Study on the MSC Dataset
DOI:
https://doi.org/10.53469/jtpes.2023.03(12).05Keywords:
Macro-management, StarCraft, Attnetion Mechanism, Graph Neural NetworksAbstract
Macro-management is a crucial aspect of real-time strategy (RTS) games like StarCraft II, which involves high-level decision-making processes such as resource allocation, unit production, and technology development. The MSC dataset, as presented in the original paper, provided an initial platform for researchers to investigate macro-management tasks using deep learning models. However, there are limitations to the dataset and existing baseline models that call for improvement. In this paper, we discuss the challenges and opportunities in enhancing macro-management research in StarCraft II. We propose improvements to the dataset by incorporating new features, addressing data imbalance, and updating preprocessing techniques. Furthermore, we review recent research and state-of-the-art methods in RTS games, such as attention mechanisms, graph neural networks, and reinforcement learning, that could be applied to improve existing tasks or introduce new research directions. We also present our experimental results, highlighting the effectiveness of the proposed improvements and novel approaches. Our goal is to inspire the research community to explore advanced AI techniques and strategies in macro-management and contribute to the development of more capable AI agents in complex RTS games.
References
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. nature, 518(7540), 529-533.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. nature, 529(7587), 484-489.
Lample, G., & Chaplot, D. S. (2017, February). Playing FPS games with deep reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1).
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., ... & Kavukcuoglu, K. (2016, June). Asynchronous methods for deep reinforcement learning. In International conference on machine learning (pp. 1928-1937). PMLR.
Peng, P., Wen, Y., Yang, Y., Yuan, Q., Tang, Z., Long, H., & Wang, J. (2017). Multiagent bidirectionally-coordinated nets: Emergence of human-level coordination in learning to play starcraft combat games. arXiv preprint arXiv:1703.10069.
Justesen, N., & Risi, S. (2017, August). Learning macromanagement in starcraft from replays using deep learning. In 2017 IEEE Conference on Computational Intelligence and Games (CIG) (pp. 162-169). IEEE.
Erickson, G., & Buro, M. (2014). Global state evaluation in StarCraft. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 10, No. 1, pp. 112-118).
Churchill, D., & Buro, M. (2011, October). Build order optimization in starcraft. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 7, No. 1, pp. 14-19).
Synnaeve, G., & Bessiere, P. (2011, October). A Bayesian model for plan recognition in RTS games applied to StarCraft. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 7, No. 1, pp. 79-84).
Weber, B. G., & Mateas, M. (2009, September). A data mining approach to strategy prediction. In 2009 IEEE Symposium on Computational Intelligence and Games (pp. 140-147). IEEE.
Cho, H. C., Kim, K. J., & Cho, S. B. (2013, August). Replay-based strategy prediction and build order adaptation for StarCraft AI bots. In 2013 IEEE Conference on Computational Inteligence in Games (CIG) (pp. 1-7). IEEE.
Lin, Z., Gehring, J., Khalidov, V., & Synnaeve, G. (2017). Stardata: A starcraft ai research dataset. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 13, No. 1, pp. 50-56).
Stanescu, M., Barriga, N. A., Hess, A., & Buro, M. (2016, September). Evaluating real-time strategy game states using convolutional neural networks. In 2016 IEEE Conference on Computational Intelligence and Games (CIG) (pp. 1-7). IEEE.
Ravari, Y. N., Bakkes, S., & Spronck, P. (2016). Starcraft winner prediction. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 12, No. 2, pp. 2-8).
Sánchez-Ruiz, A. A., & Miranda, M. (2017). A machine learning approach to predict the winner in StarCraft based on influence maps. Entertainment Computing, 19, 29-41.
Hsieh, J. L., & Sun, C. T. (2008, June). Building a player strategy model by analyzing replays of real-time strategy games. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (pp. 3106-3111). IEEE.
Churchill, D., & Buro, M. (2011, October). Build order optimization in starcraft. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 7, No. 1, pp. 14-19).
Köstler, H., & Gmeiner, B. (2013). A multi-objective genetic algorithm for build order optimization in StarCraft II. KI-Künstliche Intelligenz, 27(3), 221-233.
Blackford, J., & Lamont, G. (2014). The real-time strategy game multi-objective build order problem. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 10, No. 1, pp. 105-111).
Synnaeve, G., & Bessiere, P. (2012). A dataset for StarCraft AI and an example of armies clustering. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Vol. 8, No. 3, pp. 25-30).
Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., & Khudanpur, S. (2010, September). Recurrent neural network based language model. In Interspeech (Vol. 2, No. 3, pp. 1045-1048).
Cho, K., Van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259.
Y. Wu, T. Gao, S.Wang and Z. Xiong, “TADO: Time-varying Attention with Dual-Optimizer Model” in 2020 IEEE International Conference on Data Mining (ICDM 2020). IEEE, 2020, Sorrento, Italy, 2020, pp. 1340-1345, doi: 10.1109/ICDM50108.2020.00174.
S.Wang and B.Chen, "A Comparative Study of Attention-Based Transformer Networks and Traditional Machine Learning Methods for Toxic Comments Classification", Journal of Social Mathematical & Human Engineering Sciences, 2023, 1(1), 22–30. doi: https://doi.org/10.31586/jsmhes.2023.697
S.Wang, B.Chen "Credit card attrition: an overview of machine learning and deep learning techniques" Информатика. Экономика. Управление/Informatics. Economics. Management. 2023, 2(4), 0134–0144, doi: 10.47813/2782-5280-2023-2-4-0134-0144
S.Wang, B.Chen "A deep learning approach to diabetes classification using attention-based neural network and generative adversarial network" MODERN RESEARCH:TOPICAL ISSUES OF THEORY AND PRACTICE, vol 5, 37-41
S.Wang, B.Chen "TopoDimRed: a novel dimension reduction technique for topological data analysis", Информатика. Экономика. Управление/Informatics. Economics. Management. 2023, 2(2), 0201-0213, doi: 10.47813/2782-5280-2023-2-2-0201-0213
Che, C., Liu, B., Li, S., Huang, J., & Hu, H. (2023). Deep learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10), 36-41.
Hu, H., Li, S., Huang, J., Liu, B., & Che, C. (2023). Casting product image data for quality inspection with xception and data augmentation. Journal of Theory and Practice of Engineering Science, 3(10), 42-46.
Tianbo, S., Weijun, H., Jiangfeng, C., Weijia, L., Quan, Y., & Kun, H. (2023, January). Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition. In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) (pp. 834-837). IEEE.
Mou, C., Ye, X., Wu, J., & Dai, W. (2023, April). Automated ICD Coding Based on Neural Machine Translation. In 2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) (pp. 495-500). IEEE.
Mou, C., Dai, W., Ye, X., & Wu, J. (2023, July). Research On Method Of User Preference Analysis Based on Entity Similarity and Semantic Assessment. In 2023 8th International Conference on Signal and Image Processing (ICSIP) (pp. 1029-1033). IEEE.
Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281-285.
Che, C., Hu, H., Zhao, X., Li, S., & Lin, Q. (2023). Advancing Cancer Document Classification with R andom Forest. Academic Journal of Science and Technology, 8(1), 278-280.
Liu, B., Yu, L., Che, C., Lin, Q., Hu, H., & Zhao, X. (2023). Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms. arXiv preprint arXiv:2312.12872.
Chang Che, Qunwei Lin, Xinyu Zhao, Jiaxin Huang, and Liqiang Yu. 2023. Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation. In Proceedings of the 2023 6th International Conference on Big Data Technologies (ICBDT '23). Association for Computing Machinery, New York, NY, USA, 414–418. https://doi.org/10.1145/3627377.3627442
Zhang, Q., Cai, G., Cai, M., Qian, J., & Song, T. (2023). Deep Learning Model Aids Breast Cancer Detection. Frontiers in Computing and Intelligent Systems, 6(1), 99-102.
Xu, J., Pan, L., Zeng, Q., Sun, W., & Wan, W. (2023). Based on TPUGRAPHS Predicting Model Runtimes Using Graph Neural Networks. Frontiers in Computing and Intelligent Systems, 6(1), 66-69.
Hong, Z., Yan, L., Jize, X., Yixu, W., Yuxiang, L. (2023). Improvement of Deep Learning Model for Gastrointestinal Tract Segmentation Surgery. Frontiers in Computing and Intelligent Systems, 6(1), 103-106.
Miao, T., Zepeng, S., Xingnan, W., Kuo, W., & Yuxiang, L. (2023). The Application of Artificial Intelligence in Medical Diagnostics: A New Frontier. Academic Journal of Science and Technology, 8(2), 57-61.
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Copyright (c) 2023 Yanqi Zong, Luqi Lin, Sihao Wang, Zhengrong Cui, Yizhi Chen
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