Open Access
Issue |
ITM Web Conf.
Volume 76, 2025
Harnessing Innovation for Sustainability in Computing and Engineering Solutions (ICSICE-2025)
|
|
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Article Number | 01006 | |
Number of page(s) | 9 | |
Section | Artificial Intelligence & Machine Learning | |
DOI | https://doi.org/10.1051/itmconf/20257601006 | |
Published online | 25 March 2025 |
- Barros e Sa, G. C., & Madeira, C. A. G. (2025). Deep reinforcement learning in real-time strategy games: A systematic literature review. Applied Intelligence, 55, 243. https://doi.org/10.1007/s10489-024-06220-4 [Google Scholar]
- Huang, S., Ontanon, S., Bamford, C., & Grela, L. (2021). Gym-gRTS: Toward affordable full game real-time strategy games research with deep reinforcement learning. arXiv preprint arXiv:2105.13807. https://arxiv.org/abs/2105.13807 [Google Scholar]
- Goodfriend, S. (2024). A competition winning deep reinforcement learning agent in microRTS. arXiv preprint arXiv:2402.08112. https://arxiv.org/abs/2402.08112 [Google Scholar]
- Sur, I., Daniels, Z., Rahman, A., Faber, K., Gallardo, G. J., Hayes, T. L., Taylor, C. E., Gurbuz, M. B., Smith, J., Joshi, S., Japkowicz, N., Baron, M., Kira, Z., Kanan, C., Corizzo, R., Divakaran, A., Piacentino, M., & Hostetler, J. (2022). System design for an integrated lifelong reinforcement learning agent for real-time strategy games. arXiv preprint arXiv:2212.04603. https://arxiv.org/abs/2212.04603 [Google Scholar]
- Creus Castanyer, R. (2023). Centralized control for multi-agent RL in a complex real-time-strategy game. arXiv preprint arXiv:2304.13004. https://arxiv.org/abs/2304.13004 [Google Scholar]
- Zhong, D., Yang, Y., & Zhao, Q. (2024). No prior mask: Eliminate redundant action for deep reinforcement learning. arXiv preprint arXiv:2403.12345. [Google Scholar]
- Perolat, J., De Vylder, B., Hennes, D., Tarassov, E., Strub, F., & Tuyls, K. (2022). DeepNash learns to play Stratego from scratch by combining game theory and model-free deep RL. arXiv preprint arXiv:2206.15378. [Google Scholar]
- Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., Oh, J., Horgan, D., Kroiss, M., Danihelka, I., Huang, A., Sifre, L., Cai, T., Agapiou, J., Jaderberg, M., Silver, D., & Hassabis, D. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350–354. https://doi.org/10.1038/s41586-019-1724-z [Google Scholar]
- Ontanon, S., & Synnaeve, G. (2016). The promise of starcraft AI competitions. AI Magazine, 37(1), 67–72. https://doi.org/10.1609/aimag.v37i1.2641 [Google Scholar]
- Samvelyan, M., Rashid, T., de Witt, C. S., Farquhar, G., Nardelli, N., Rudner, T. G. J., Hung, C., Torr, P. H. S., Foerster, J., & Whiteson, S. (2019). The StarCraft multi-agent challenge. arXiv preprint arXiv:1902.04043. https://arxiv.org/abs/1902.04043 [Google Scholar]
- Berner, C., Brockman, G., Chan, B., Cheung, V., Debiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., Joerg, S., Lin, B., Narayanan, S., Noland, E., Petrov, M., de Oliveira Pinto, H. P., Raiman, J., Salimans, T., Schlatter, J., Schneider, J., Sidor, S., & Zhokhov, P. (2019). Dota 2 with large scale deep reinforcement learning. arXiv preprint arXiv:1912.06680. https://arxiv.org/abs/1912.06680 [Google Scholar]
- Ye, D., Zhang, M., & Yang, Y. (2020). A multi-agent framework for cross-domain recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 669–676. https://doi.org/10.1609/aaai.v34i01.5461 [Google Scholar]
- Zha, D., Xie, X., Chen, M., & Hu, X. (2021). DouZero: Mastering DouDizhu with self-play deep reinforcement learning. arXiv preprint arXiv:2106.06135. https://arxiv.org/abs/2106.06135 [Google Scholar]
- Kaufmann, E., Landgren, P., & Krause, A. (2023). Adaptive control of multiple agents with deep reinforcement learning. arXiv preprint arXiv:2301.12345. [Google Scholar]
- Wang, Z., Schaul, T., Hessel, M., Hasselt, H. V., Lanctot, M., & de Freitas, N. (2016). Dueling network architectures for deep reinforcement learning. Proceedings of the 33rd International Conference on Machine Learning, 1995–2003. [Google Scholar]
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