S. Ontanón, G. Synnaeve, A. Uriarte, F. Richoux, D. Churchill, and M. Preuss. A survey of real-time strategy game ai research and competition in starcraft. IEEE Transactions on Computational Intelligence and AI in games, 5(4):293-311, 2013.
S. Ontañón, K. Mishra, N. Sugandh, and A. Ram. Learning from demonstration and case-based planning for real-time strategy games. Springer Berlin Heidelberg, 2008.
Z.-J. Pang, R.-Z. Liu, Z.-Y. Meng, Y. Zhang, Y. Yu, and T. Lu. On reinforcement learning for full-length game of starcraft. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4691-4698, 2019.
R. Raileanu, E. Denton, A. Szlam, and R. Fergus. Modeling others using oneself in multi-agent reinforcement learning. In International conference on machine learning, pages 4257-4266. PMLR, 2018.
T. Schaul, D. Horgan, K. Gregor, and D. Silver. Universal value function approximators. In International conference on machine learning, pages 1312-1320. PMLR, 2015.
P. Sun, X. Sun, L. Han, J. Xiong, Q. Wang, B. Li, Y. Zheng, J. Liu, Y. Liu, H. Liu, et al. Tstarbots: Defeating the cheating level built in starcraft ii in the full game. arXiv preprint arXiv:1809.07193, 2018.
N. Usunier, G. Synnaeve, Z. Lin, and S. Chintala. Episodic exploration for deep deterministic policies: An application to starcraft micromanagement tasks. arXiv preprint arXiv:1609.02993, 2016.
V. Veeriah, J. Oh, and S. Singh. Many-goals reinforcement learning. arXiv preprint arXiv:1806.09605, 2018.
O. Vinyals, T. Ewalds, S. Bartunov, P. Georgiev, A. S. Vezhnevets, M. Yeo, A. Makhzani, H. Kuttler, J. Agapiou, J. Schrittwieser, et al. Starcraft ii: A new challenge for reinforcement learning. arXiv preprint arXiv:1708.04782, 2017.
O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev, et al. Grandmaster level in starcraft ii using multi-agent reinforcement learning. Nature, 575(7782):350-354, 2019.
X. Wang, J. Song, P. Qi, P. Peng, Z. Tang, W. Zhang, W. Li, X. Pi, J. He, C. Gao, et al. Scc: an efficient deep reinforcement learning agent mastering the game of starcraft ii. In International Conference on Machine Learning, pages 10905-10915. PMLR, 2021.
B. Weber. Aiide 2010 starcraft competition. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 6, pages xv-xvi, 2010.
B. G. Weber, M. Mateas, and A. Jhala. Building human-level ai for real-time strategy games. Aaai Fall Symposium, 2011.
T. Willi, A. H. Letcher, J. Treutlein, and J. Foerster. Cola: consistent learning with opponent-learning awareness. In International Conference on Machine Learning, pages 23804-23831. PMLR, 2022.
S. Wu, J. Yao, H. Fu, Y. Tian, C. Qian, Y. Yang, Q. Fu, and Y. Wei. Quality-similar diversity via population based reinforcement learning. In The Eleventh International Conference on Learning Representations, 2022.
J. Yao, W. Liu, H. Fu, Y. Yang, S. McAleer, Q. Fu, and W. Yang. Policy space diversity for non-transitive games. arXiv preprint arXiv:2306.16884, 2023.
Y. Zheng, Z. Meng, J. Hao, Z. Zhang, T. Yang, and C. Fan. A deep bayesian policy reuse approach against non-stationary agents. Advances in neural information processing systems, 31, 2018.