A Distributed Learning Control System for Elevator Groups
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.
KeywordsService Time Reinforcement Learning Reinforcement Learning Algorithm Distribute Control System Elevator Group
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- 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
- 5.Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar
- 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
- 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.Watkins, C.J.C.H.: Learning from Delayed Rewards. PhD thesis, King’s College, Cambridge (1989)Google Scholar