A Distributed Learning Control System for Elevator Groups

  • Tomasz Walczak
  • Paweł Cichosz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


Human-designed elevator control policies usually perform sufficiently well, but are costly to obtain and do not easily adapt to changing traffic patterns. This paper describes an adaptive distributed elevator control system based on reinforcement learning. Whereas inspired by prior work, the design of the system is novel, developed with the intention to avoid any unrealistic assumptions that would limit its practical usefulness. Encouraging experimental results are presented with a realistic simulator of an elevator group.


Service Time Reinforcement Learning Reinforcement Learning Algorithm Distribute Control System Elevator Group 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Levy, D., Yardin, M., Alexandrowitz, A.: Optimal control of elevators. International Journal of Systems Science 8, 301–320 (1977)CrossRefGoogle Scholar
  2. 2.
    Pepyne, D.L., Cassandras, C.G.: Optimal dispatching control for elevator systems during uppeak traffic. IEEE Transactions on Control Systems Technology 5(6), 629–643 (1997)CrossRefGoogle Scholar
  3. 3.
    Siikonen, M.: Planning and Control Models for Elevators in High-Rise Buildings. PhD thesis, Helsinki Unverstity of Technology, Systems Analysis Laboratory (1997)Google Scholar
  4. 4.
    Cortés, P., Larrañeta, J., Onieva, L.: Genetic algorithm for controllers in elevator groups: Analysis and simulation during lunchpeak traffic. Applied Soft Computing 4, 159–174 (2004)CrossRefGoogle Scholar
  5. 5.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
  6. 6.
    Crites, R.H., Barto, A.G.: Improving elevator performance using reinforcement learning. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 1017–1023. MIT Press, Cambridge (1996)Google Scholar
  7. 7.
    Crites, R.H., Barto, A.G.: Elevator group control using multiple reinforcement learning agents. Machine Learning 33, 235–262 (1998)MATHCrossRefGoogle Scholar
  8. 8.
    Bradtke, S.J., Duff, M.O.: Reinforcement learning methods for continuous-time Markov decision problems. In: Tesauro, G., Touretzky, D., Leen, T. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 393–400. MIT Press, Cambridge (1995)Google Scholar
  9. 9.
    Watkins, C.J.C.H.: Learning from Delayed Rewards. PhD thesis, King’s College, Cambridge (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tomasz Walczak
    • 1
  • Paweł Cichosz
    • 2
  1. 1.Institute of Fundamental Technological Research, Polish Academy of SciencesWarsawPoland
  2. 2.Department of Electronics and Information TechnologyWarsaw University of TechnologyWarsawPoland

Personalised recommendations