Multi-robot systems with agent-based reinforcement learning: evolution, opportunities and challenges

Erfu Yang, Dongbing Gu

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Multi-agent reinforcement learning for multi-robot systems is a challenging issue in both robotics and artificial intelligence. With the ever increasing interests in theoretical researches and practical applications, currently there have been a lot of efforts towards providing good solutions to this challenge. However, there are still many difficulties in scaling up multi-agent reinforcement learning to multi-robot systems. This paper presents a survey on the evolution, opportunities and challenges of applying agent-based reinforcement learning to multi-robot systems. After reviewing some important advances in this field, some challenging problems and promising research directions are focused on. A concluding remark is made from the perspectives of the authors.

Original languageEnglish
Pages (from-to)271-286
Number of pages16
JournalInternational Journal of Modelling, Identification and Control
Issue number4
Publication statusPublished - 12 May 2009


  • approximation and generalisation
  • fuzzy logic
  • MRSs
  • multi-agent systems
  • multi-robot systems
  • reinforcement learning
  • stochastic games
  • survey

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