Skip to main content
SpringerLink
Log in
Book cover

International Conference on Neural Information Processing

ICONIP 2021: Neural Information Processing pp 344–352Cite as

Combat Unit Selection Based on Hybrid Neural Network in Real-Time Strategy Games

  • Conference paper
  • First Online:

  • 1766 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1517))

Abstract

Real-Time Strategy (RTS) games are one of the most complex and challenging areas from the perspective of artificial intelligence. Besides, some real-time wargaming platforms not only have the characteristics of large state-action space, incomplete information, and instantaneity but also have not many decision-making in the whole game. In this paper, we propose a data augmentation method and a hybrid neural network model combining Gated Recurrent Unit(GRU) network and Pointer network, which can select an action unit to execute the decision and a target unit to be attacked at a few time points in the game. The hybrid neural network model and data augmentation method are evaluated in a war simulation platform. Experimental results show that in the unit selection of real-time strategy games, the hybrid network model and data augmentation method outperform the hybrid neural network without data augmentation or simply using pointer network model.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Barriga, N., Stanescu, M., Buro, M.: Puppet search: enhancing scripted behavior by look-ahead search with applications to real-time strategy games. In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, vol. 11, pp. 9–15 (2015)

    Google Scholar 

  2. Barriga, N., Stanescu, M., Buro, M.: Combining strategic learning with tactical search in real-time strategy games. In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, vol. 13, pp. 9–15 (2017)

    Google Scholar 

  3. Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 1–12 (2018)

    Google Scholar 

  4. Churchill, D., Buro, M.: Portfolio greedy search and simulation for large-scale combat in StarCraft. In: 2013 IEEE Conference on Computational Intelligence in Games (CIG), pp. 1–8. IEEE (2013)

    Google Scholar 

  5. Churchill, D., Saffidine, A., Buro, M.: Fast heuristic search for RTS game combat scenarios. In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, vol. 8 (2012)

    Google Scholar 

  6. DataCastle: Intelligent game confrontation competition of joint operation (2020). https://encourage.dcjingsai.com/WarGame.html

  7. Foerster, J., et al.: Stabilising experience replay for deep multi-agent reinforcement learning. In: International Conference on Machine Learning, pp. 1146–1155. PMLR (2017)

    Google Scholar 

  8. Guo, B., Zhang, C., Liu, J., Ma, X.: Improving text classification with weighted word embeddings via a multi-channel TextCNN model. Neurocomputing 363, 366–374 (2019)

    Article  Google Scholar 

  9. Han, X., Yan, H., Zhang, J., Wang, L.: Cluster-based alpha-beta search for real-time strategy games. In: IOP Conference Series: Materials Science and Engineering, vol. 435, pp. 1–10 (2018)

    Google Scholar 

  10. OpenAI.: Openai blog: Dota 2, 17 April 2018. https://blog.openai.com/dota-2/

  11. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)

  12. Shafi, K., Abbass, H.A.: A survey of learning classifier systems in games. IEEE Comput. Intell. Mag. 12(1), 42–55 (2017)

    Article  Google Scholar 

  13. Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)

    Article  Google Scholar 

  14. Vinyals, O., et al.: Grandmaster level in StarCraft ii using multi-agent reinforcement learning. Nature 575(7782), 350–354 (2019)

    Article  Google Scholar 

  15. Vinyals, O., Fortunato, M., Jaitly, N.: Pointer networks. In: International Conference on Neural Information Processing Systems, pp. 2692–2700 (2015)

    Google Scholar 

  16. Wu, B.: Hierarchical macro strategy model for MOBA game AI. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1206–1213 (2019)

    Google Scholar 

Download references

Acknowledgement

This work was supported by National Natural Science Foundation of China (Grant No. 61502274), Natural Science Foundation of Hubei (Grant No. 2015CFB336), and Open Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering (CTGU)(Grant No. 2015KLA08)

Author information

Authors and Affiliations

  1. Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, 443002, Hubei, China

    Hongcun Guo, Zhaoxiang Zang, Zhen Zhang & Pei Tian

  2. College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China

    Hongcun Guo, Zhaoxiang Zang, Zhen Zhang & Pei Tian

Authors
  1. Hongcun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaoxiang Zang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pei Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaoxiang Zang .

Editor information

Editors and Affiliations

  1. Sampoerna University, Jakarta, Indonesia

    Teddy Mantoro

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  3. Sampoerna University, Jakarta, Indonesia

    Media Anugerah Ayu

  4. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  5. Universitas Indonesia, Depok, Indonesia

    Achmad Nizar Hidayanto

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Guo, H., Zang, Z., Zhang, Z., Tian, P. (2021). Combat Unit Selection Based on Hybrid Neural Network in Real-Time Strategy Games. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1517. Springer, Cham. https://doi.org/10.1007/978-3-030-92310-5_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-92310-5_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92309-9

  • Online ISBN: 978-3-030-92310-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation