Grandmaster level in StarCraft II using multi-agent reinforcement learning

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 am...

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Veröffentlicht in:	Nature (London) Jg. 575; H. 7782; S. 350 - 354
Hauptverfasser:	Vinyals, Oriol, Babuschkin, Igor, Czarnecki, Wojciech M., Mathieu, Michaël, Dudzik, Andrew, Chung, Junyoung, Choi, David H., Powell, Richard, Ewalds, Timo, Georgiev, Petko, Oh, Junhyuk, Horgan, Dan, Kroiss, Manuel, Danihelka, Ivo, Huang, Aja, Sifre, Laurent, Cai, Trevor, Agapiou, John P., Jaderberg, Max, Vezhnevets, Alexander S., Leblond, Rémi, Pohlen, Tobias, Dalibard, Valentin, Budden, David, Sulsky, Yury, Molloy, James, Paine, Tom L., Gulcehre, Caglar, Wang, Ziyu, Pfaff, Tobias, Wu, Yuhuai, Ring, Roman, Yogatama, Dani, Wünsch, Dario, McKinney, Katrina, Smith, Oliver, Schaul, Tom, Lillicrap, Timothy, Kavukcuoglu, Koray, Hassabis, Demis, Apps, Chris, Silver, David
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 01.11.2019 Nature Publishing Group
Schlagworte:	639/705/117 639/705/531 Actors Agents (artificial intelligence) Algorithms Artificial Intelligence Cameras Complexity Computer & video games Domains Game theory Humanities and Social Sciences Humans Learning Machine learning Methods Multiagent systems multidisciplinary Multiplayer online games Neural networks Pattern recognition Players Rankings Reinforcement Reinforcement learning (Machine learning) Reinforcement, Psychology Science Science (multidisciplinary) Technology application Video Games United Kingdom
ISSN:	0028-0836, 1476-4687, 1476-4687
Online-Zugang:	Volltext
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