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Abstract: This paper describes the application of reinforcement learning (RL) to the difficult real world problem of elevator dispatching. The elevator domain poses a combination of challenges not seen in most RL research to date. Elevator systems operate in continuous state spaces and in continuous time as discrete event dynamic systems. Their states are not fully observable and they are nonstationary due to changing passenger arrival rates. In addition, we use a team of RL agents, each of which is... (Update)
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BibTeX entry: (Update)
Crites R. H. and A. G. Barto, "Improving Elevator Performance Using Reinforcement Learning", in D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (Eds.) Advances in Neural Information Processing Systems. http://citeseer.ist.psu.edu/crites96improving.html More
@inproceedings{ crites96improving,
author = "Robert H. Crites and Andrew G. Barto",
title = "Improving Elevator Performance Using Reinforcement Learning",
booktitle = "Advances in Neural Information Processing Systems",
volume = "8",
publisher = "The {MIT} Press",
editor = "David S. Touretzky and Michael C. Mozer and Michael E. Hasselmo",
pages = "1017--1023",
year = "1996",
url = "citeseer.ist.psu.edu/crites96improving.html" }
Citations (may not include all citations):658 Learning from Delayed Rewards (context) - Watkins - 1989
219 Practical Issues in Temporal Difference Learning - Tesauro - 1992
110 Temporal Difference Learning and TD--Gammon (context) - Tesauro - 1995
34 Reinforcement Learning Methods for Continuous--Time Markov D.. - Bradtke, Duff - 1995
24 TD--Gammon (context) - Tesauro - 1994
6 Elevator Dispatchers for Down Peak Traffic (context) - Bao, Cassandras et al. - 1994
4 A Dynamic Load Balancing Approach to the Control of Multiser.. (context) - Lewis - 1991
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