A Real-Time Multiagent Strategy Learning Environment and Experimental Framework

  • Hongda ZhangEmail author
  • Decai Li
  • Liying Yang
  • Feng Gu
  • Yuqing He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)


Many problems in the real world can be attributed to the problem of multiagent. The study on the issue of multiagent is of great significance to solve these social problems. This paper reviews the research on multiagent based real-time strategy game environments, and introduces the multiagent learning environment and related resources. We choose a deep learning environment based on the StarCraft game as a research environment for multiagent collaboration and decision-making, and form a research mentality focusing mainly on reinforcement learning. On this basis, we design a verification platform for the related theoretical research results and finally form a set of multiagent research system from the theoretical method to the actual platform verification. Our research system has reference value for multiagent related research.


Multiagent Reinforcement learning Real-time strategy 



The authors acknowledge the support of the National Natural Science Foundation of China (grant U1608253, grant 61473282), Natural Science Foundation of Guangdong Province (2017B010116002) and this work was supported by the Youth Innovation Promotion Association, CAS. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors, and do not necessarily reflect the views of the funding organizations.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Hongda Zhang
    • 1
    • 2
    Email author
  • Decai Li
    • 2
  • Liying Yang
    • 2
  • Feng Gu
    • 2
  • Yuqing He
    • 2
  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.Shenyang Institute of Automation Chinese Academy of SciencesShenyangChina

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