The StarCraft Multi-Agent Challenge

11 Feb 2019Mikayel Samvelyan • Tabish Rashid • Christian Schroeder de Witt • Gregory Farquhar • Nantas Nardelli • Tim G. J. Rudner • Chia-Man Hung • Philip H. S. Torr • Jakob Foerster • Shimon Whiteson

A particularly challenging class of problems in this area is partially observable, cooperative, multi-agent learning, in which teams of agents must learn to coordinate their behaviour while conditioning only on their private observations. This is an attractive research area since such problems are relevant to a large number of real-world systems and are also more amenable to evaluation than general-sum problems. In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.

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