A distributed learning control system for elevator groups
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2006, PROCEEDINGS(2006)
Abstract
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.
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Key words
traffic pattern,elevator control system,unrealistic assumption,prior work,practical usefulness,reinforcement learning,elevator group,human-designed elevator control policy,realistic simulator,control system
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