Multi-agent reinforcement learning for character control

Cheng Li, Levi Fussell, Taku Komura

Research output: Contribution to journalArticlepeer-review


Simultaneous control of multiple characters has been a research topic that has been extensively pursued for applications in computer games and computer animations, for applications such as crowd simulation, controlling two characters carrying objects or fighting with one another and controlling a team of characters playing collective sports. With the advance in deep learning and reinforcement learning, there is a growing interest in applying multi-agent reinforcement learning for intelligently controlling the characters to produce realistic movements. In this paper we will survey the state-of-the-art MARL techniques that are applicable for character control. We will then survey papers that make use of MARL for multi-character control and then discuss about the possible future directions of research.
Original languageEnglish
Pages (from-to)3115-3123
Number of pages9
JournalThe Visual Computer
Issue number12
Publication statusPublished - 4 Dec 2021


  • Multi-agent reinforcement learning
  • Character control
  • Computer animation


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