Multi-agent Policy Learning
Multi-Agent Policy Learning is a computational framework within reinforcement learning that models the simultaneous adaptation of decision-making strategies by multiple interacting agents in shared environments, distinguished by its focus on the emergence of joint policies through decentralized learning and strategic interdependence.
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2017–2026
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
Oriol Vinyals, I. Babuschkin, Wojciech Marian Czarnecki et al. · Nature · 2019 · 3.4K citations
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Northwestern Polytechnical University
Xi'an, China
16 h-index
50 publications
3.4K citations

Imperial College London
London, United Kingdom
13 h-index
34 publications
2.3K citations

Peking University
Beijing, China
12 h-index
35 publications
1.4K citations
Carnegie Mellon University
Pittsburgh, United States
12 h-index
25 publications
3K citations
Tsinghua University
Beijing, China
12 h-index
27 publications
1.5K citations

Shanghai Jiao Tong University
Shanghai, China
11 h-index
29 publications
1.1K citations
Nanyang Technological University
Singapore, Singapore
10 h-index
22 publications
1.5K citations

University of Electronic Science and Technology of China
Chengdu, China
10 h-index
30 publications
1.9K citations
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Repository · 86 publications · 5.3K citations
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Conference · 13 publications · 708 citations
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