Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions1,2,3, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems4. Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players. We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies, each represented by deep neural networks5,6. We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Frontiers of Physics Open Access 24 November 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
All the games that AlphaStar played online can be found in the file ‘replays.zip’ in the Supplementary Data, and the raw data from the Battle.net experiment can be found in ‘bnet.json’ in the Supplementary Data.
The StarCraft II environment was open sourced in 2017 by Blizzard and DeepMind7. All the human replays used for imitation learning can be found at https://github.com/Blizzard/s2client-proto. The pseudocode for the supervised learning, reinforcement learning, and multi-agent learning components of AlphaStar can be found in the file ‘pseudocode.zip’ in the Supplementary Data. All the neural architecture details and hyper-parameters can be found in the file ‘detailed-architecture.txt’ in the Supplementary Data.
AIIDE StarCraft AI Competition. https://www.cs.mun.ca/dchurchill/starcraftaicomp/.
Student StarCraft AI Tournament and Ladder. https://sscaitournament.com/.
Starcraft 2 AI ladder. https://sc2ai.net/.
Churchill, D., Lin, Z. & Synnaeve, G. An analysis of model-based heuristic search techniques for StarCraft combat scenarios. in Artificial Intelligence and Interactive Digital Entertainment Conf. (AAAI, 2017).
Sutton, R. & Barto, A. Reinforcement Learning: An Introduction (MIT Press, 1998).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Vinyals, O. et al. StarCraft II: a new challenge for reinforcement learning. Preprint at https://arxiv.org/abs/1708.04782 (2017).
Vaswani, A. et al. Attention is all you need. Adv. Neural Information Process. Syst. 30, 5998–6008 (2017).
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).
Mikolov, T., Karafiat, M., Burget, L., Cernocky, J. & Khudanpur, S. Recurrent neural network based language model. INTERSPEECH-2010 1045–1048 (2010).
Metz, L., Ibarz, J., Jaitly, N. & Davidson, J. Discrete sequential prediction of continuous actions for deep RL. Preprint at https://arxiv.org/abs/1705.05035v3 (2017).
Vinyals, O., Fortunato, M. & Jaitly, N. Pointer networks. Adv. Neural Information Process. Syst. 28, 2692–2700 (2015).
Mnih, V. et al. Asynchronous methods for deep reinforcement learning. Proc. Machine Learning Res. 48, 1928–1937 (2016).
Espeholt, L. et al. IMPALA: scalable distributed deep-RL with importance weighted actor-learner architectures. Proc. Machine Learning Res. 80, 1407–1416 (2018).
Wang, Z. et al. Sample efficient actor-critic with experience replay. Preprint at https://arxiv.org/abs/1611.01224v2 (2017).
Sutton, R. Learning to predict by the method of temporal differences. Mach. Learn. 3, 9–44 (1988).
Oh, J., Guo, Y., Singh, S. & Lee, H. Self-Imitation Learning. Proc. Machine Learning Res. 80, 3875–3884 (2018).
Silver, D. et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362, 1140–1144 (2018).
Balduzzi, D. et al. Open-ended learning in symmetric zero-sum games. Proc. Machine Learning Res. 97, 434–443 (2019).
Brown, G. W. Iterative solution of games by fictitious play. Act. Anal. Prod. Alloc. 13, 374–376 (1951).
Leslie, D. S. & Collins, E. J. Generalised weakened fictitious play. Games Econ. Behav. 56, 285–298 (2006).
Heinrich, J., Lanctot, M. & Silver, D. Fictitious self-play in extensive-form games. Proc. Intl Conf. Machine Learning 32, 805–813 (2015).
Jouppi, N. P. et al. In-datacenter performance analysis of a tensor processing unit. Preprint at https://arxiv.org/abs/1704.04760v1 (2017).
Elo, A. E. The Rating of Chessplayers, Past and Present (Arco, 2017).
Campbell, M., Hoane, A. & Hsu, F. Deep Blue. Artif. Intell. 134, 57–83 (2002).
Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).
Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).
Pathak, D., Agrawal, P., Efros, A. A. & Darrell, T. Curiosity-driven exploration by self-supervised prediction. Proc. IEEE Conf. Computer Vision Pattern Recognition Workshops 16–17 (IEEE, 2017).
Jaderberg, M. et al. Human-level performance in 3D multiplayer games with population-based reinforcement learning. Science 364, 859–865 (2019).
OpenAI OpenAI Five. https://blog.openai.com/openai-five/ (2018).
Buro, M. Real-time strategy games: a new AI research challenge. Intl Joint Conf. Artificial Intelligence 1534–1535 (2003).
Samvelyan, M. et al. The StarCraft multi-agent challenge. Intl Conf. Autonomous Agents and MultiAgent Systems 2186–2188 (2019).
Zambaldi, V. et al. Relational deep reinforcement learning. Preprint at https://arxiv.org/abs/1806.01830v2 (2018).
Usunier, N., Synnaeve, G., Lin, Z. & Chintala, S. Episodic exploration for deep deterministic policies: an application to StarCraft micromanagement tasks. Preprint at https://arxiv.org/abs/1609.02993v3 (2017).
Weber, B. G. & Mateas, M. Case-based reasoning for build order in real-time strategy games. AIIDE ’09 Proc. 5th AAAI Conf. Artificial Intelligence and Interactive Digital Entertainment 106–111 (2009).
Buro, M. ORTS: a hack-free RTS game environment. Intl Conf. Computers and Games 280–291 (Springer, 2002).
Churchill, D. SparCraft: open source StarCraft combat simulation. https://code.google.com/archive/p/sparcraft/ (2013).
Weber, B. G. AIIDE 2010 StarCraft competition. Artificial Intelligence and Interactive Digital Entertainment Conf. (2010).
Uriarte, A. & Ontañón, S. Improving Monte Carlo tree search policies in StarCraft via probabilistic models learned from replay data. Artificial Intelligence and Interactive Digital Entertainment Conf. 101–106 (2016).
Hsieh, J.-L. & Sun, C.-T. Building a player strategy model by analyzing replays of real-time strategy games. IEEE Intl Joint Conf. Neural Networks 3106–3111 (2008).
Synnaeve, G. & Bessiere, P. A Bayesian model for plan recognition in RTS games applied to StarCraft. Artificial Intelligence and Interactive Digital Entertainment Conf. 79–84 (2011).
Shao, K., Zhu, Y. & Zhao, D. StarCraft micromanagement with reinforcement learning and curriculum transfer learning. IEEE Trans. Emerg. Top. Comput. Intell. 3, 73–84 (2019).
Facebook CherryPi. https://torchcraft.github.io/TorchCraftAI/.
Berkeley Overmind. https://www.icsi.berkeley.edu/icsi/news/2010/10/klein-berkeley-overmind (2010).
Justesen, N. & Risi, S. Learning macromanagement in StarCraft from replays using deep learning. IEEE Conf. Computational Intelligence and Games (CIG) 162–169 (2017).
Synnaeve, G. et al. Forward modeling for partial observation strategy games—a StarCraft defogger. Adv. Neural Information Process. Syst. 31, 10738–10748 (2018).
Farooq, S. S., Oh, I.-S., Kim, M.-J. & Kim, K. J. StarCraft AI competition report. AI Mag. 37, 102–107 (2016).
Sun, P. et al. TStarBots: defeating the cheating level builtin AI in StarCraft II in the full game. Preprint at https://arxiv.org/abs/1809.07193v3 (2018).
Schulman, J., Wolski, F., Dhariwal, P., Radford, A. & Klimov, O. Proximal policy optimization algorithms. Preprint at https://arxiv.org/abs/1707.06347v2 (2017).
Ibarz, B. et al. Reward learning from human preferences and demonstrations in Atari. Adv. Neural Information Process. Syst. 31, 8011–8023 (2018).
Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W. & Abbeel, P. Overcoming exploration in reinforcement learning with demonstrations. IEEE Intl Conf. Robotics and Automation 6292–6299 (2018).
Christiano, P. F. et al. Deep reinforcement learning from human preferences. Adv. Neural Information Process. Syst. 30, 4299–4307 (2017).
Lanctot, M. et al. A unified game-theoretic approach to multiagent reinforcement learning. Adv. Neural Information Process. Syst. 30, 4190–4203 (2017).
Perez, E., Strub, F., De Vries, H., Dumoulin, V. & Courville, A. FiLM: visual reasoning with a general conditioning layer. Preprint at https://arxiv.org/abs/1709.07871v2 (2018).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. Proc. IEEE Conf. Computer Vision and Pattern Recognition 770–778 (2016).
Hinton, G., Vinyals, O. & Dean, J. Distilling the knowledge in a neural network. Preprint at https://arxiv.org/abs/1503.02531v1 (2015).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980v9 (2014).
Bishop, C. M. Pattern Recognition and Machine Learning (Springer, 2006).
Rusu, A. A. et al. Policy distillation. Preprint at https://arxiv.org/abs/1511.06295 (2016).
Parisotto, E., Ba, J. & Salakhutdinov, R. Actor-mimic: deep multitask and transfer reinforcement learning. Preprint at https://arxiv.org/abs/1511.06342 (2016).
Precup, D., Sutton, R. S. & Singh, S. P. Eligibility traces for off-policy policy evaluation. ICML ’00 Proc. 17th Intl Conf. Machine Learning 759–766 (2016).
DeepMind Research on Ladder. https://starcraft2.com/en-us/news/22933138 (2019).
Vinyals, O. et al. AlphaStar: mastering the real-time strategy game StarCraft II https://deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii (DeepMind, 2019).
We thank Blizzard for creating StarCraft and for their continued support of the research environment, and for enabling AlphaStar to participate in Battle.net. In particular, we thank A. Hudelson, C. Lee, K. Calderone, and T. Morten. We also thank StarCraft II professional players G. ‘MaNa’ Komincz and D. ‘Kelazhur’ Schwimer for their StarCraft expertise and advice. We thank A. Cain, A. Razavi, D. Toyama, D. Balduzzi, D. Fritz, E. Aygün, F. Strub, G. Ostrovski, G. Alain, H. Tang, J. Sanchez, J. Fildes, J. Schrittwieser, J. Novosad, K. Simonyan, K. Kurach, P. Hamel, R. Barreira, S. Reed, S. Bartunov, S. Mourad, S. Gaffney, T. Hubert, the team that created PySC2 and the whole DeepMind Team, with special thanks to the research platform team, comms and events teams, for their support, ideas, and encouragement.
M.J., W.M.C., O.V., and D.S. have filed provisional patent application 62/796,567 about the contents of this manuscript. The remaining authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Peer review information Nature thanks Dave Churchill, Santiago Ontanon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Extended data figures and tables
Top, win probability of AlphaStar Supervised against itself, when applying various agent action rate limits. Our limit does not affect supervised performance and is acceptable when compared to humans. Bottom, distributions of APMs of AlphaStar Final (blue) and humans (red) during games on Battle.net. Dashed lines show mean values.
Left, distribution of delays between when the game generates an observation and when the game executes the corresponding agent action. Right, distribution of how long agents request to wait without observing between observations.
A detailed description is provided in the Supplementary Data, Detailed Architecture.
Units built by Protoss AlphaStar Supervised (left) and AlphaStar Final (right) over multiple self-play games. AlphaStar Supervised can build every unit.
PFSP-based training outperforms FSP under all measures considered: it has a stronger population measured by relative population performance, provides a less exploitable solution, and has better final agent performance against the corresponding league.
Diagram of the training setup for the entire league.
Top, visualization of all the matches played by AlphaStar Final (right) and matches against opponents above 4,500 MMR of AlphaStar Mid (left). Each Gaussian represents an opponent MMR (with uncertainty): AlphaStar won against opponents shown in green and lost to those shown in red. Blue is our MMR estimate, and black is the MMR reported by StarCraft II. The orange background is the Grandmaster league range. Bottom, win probability versus gap in MMR. The shaded grey region shows MMR model predictions when players’ uncertainty is varied. The red and blue line are empirical win rates for players above 6,000 MMR and AlphaStar Final, respectively. Both human and AlphaStar win rates closely follow the MMR model.
Extended Data Fig. 8 Payoff matrix (limited to only Protoss versus Protoss games for simplicity) split into agent types of the league.
Blue means a row agent wins, red loses, and white draws. The main agents behave transitively: the more recent agents win consistently against older main agents and exploiters. Interactions between exploiters are highly non-transitive: across the full payoff, there are around 3,000,000 rock–paper–scissor cycles (with requirement of at least 70% win rates to form a cycle) that involve at least one exploiter, and around 200 that involve only main agents.
About this article
Cite this article
Vinyals, O., Babuschkin, I., Czarnecki, W.M. et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature 575, 350–354 (2019). https://doi.org/10.1038/s41586-019-1724-z
This article is cited by
Frontiers of Physics (2024)
Nature Reviews Physics (2023)
Nature Reviews Neuroscience (2023)