Study of a Multi-Robot Collaborative Task through Reinforcement Learning

  • Juan Pereda
  • Manuel Martín-Ortiz
  • Javier de Lope
  • Félix de la Paz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)


A open issue in multi-robots systems is coordinating the collaboration between several agents to obtain a common goal. The most popular solutions use complex systems, several types of sensors and complicated controls systems. This paper describes a general approach for coordinating the movement of objects by using reinforcement learning. Thus, the method proposes a framework in which two robots are able to work together in order to achieve a common goal. We use simple robots without any kind of internal sensors and they only obtain information from a central camera. The main objective of this paper is to define and to verify a method based on reinforcement learning for multi-robot systems, which learn to coordinate their actions for achieving common goal.


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  1. 1.
    Martin, J.A., de Lope, J., Maravall, D.: Analysis and solution of a predator-protector-prey multi-robot system by a high-level reinforcement learning architecture and adaptive systems theory. Neurocomputing 58(12), 1266–1272 (2010)Google Scholar
  2. 2.
    Iima, H., Kuroe, Y.: Swarm Reinforcement Learning Algortithms Based on Sarsa Method. In: SICE Annual Conference (2008)Google Scholar
  3. 3.
    Yang, E., Gu, D.: Multiagent Reinforcement Learning for Multi-Robot Systems: A Survey. CSM-404. Technical Reports of the Department of Computer Science, University of Essex (2004)Google Scholar
  4. 4.
    Matarić, M.J.: Coordination and learning in Multi-Robot Systems. IEEE Intelligent Systems, 6–8 (1998)Google Scholar
  5. 5.
    Matarić, M.J.: Reinforcement Learning in the Multi-Robot Domain. Autonomous Robots 4(1), 73–83 (1997)CrossRefGoogle Scholar
  6. 6.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
  7. 7.
    Maravall, D., De Lope, J., Martín H, J.A.: Hybridizing evolutionary computation and reinforcement learning for the design of almost universal controllers for autonomous robots. Neurocomputing 72(4-6), 887–894 (2009)CrossRefGoogle Scholar
  8. 8.
    Sutton, R.S.: Reinforcement learning architectures. In: Proc. Int. Symp. on Neural Information Processing, Kyushu Inst. of Technology, Japan (1992)Google Scholar
  9. 9.
    Webots. Commercial Mobile Robot Simulation Software,

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Juan Pereda
    • 1
  • Manuel Martín-Ortiz
    • 1
  • Javier de Lope
    • 2
  • Félix de la Paz
    • 3
  1. 1.ITRB Labs ResearchTechnology Development and Innovation, S.L.Spain
  2. 2.Computational Cognitive RoboticsUniversidad Politécnica de MadridSpain
  3. 3.Dept. Artificial IntelligenceUNEDSpain

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