Novel RL approach for efficient Elevator Group Control Systems

Nathan Vaartjes; Vincent Francois-Lavet

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arXiv · 2507.00011

Novel RL approach for efficient Elevator Group Control Systems

Nathan Vaartjes·Vincent Francois-Lavet

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Abstract

Efficient elevator traffic management in large buildings is critical for minimizing passenger travel times and energy consumption. Because heuristic- or pattern-detection-based controllers struggle with the stochastic and combinatorial nature of dispatching, we model the six-elevator, fifteen-floor system at Vrije Universiteit Amsterdam as a Markov Decision Process and train an end-to-end Reinforcement Learning (RL) Elevator Group Control System (EGCS). Key innovations include a novel action space encoding to handle the combinatorial complexity of elevator dispatching, the introduction of infra-steps to model continuous passenger arrivals, and a tailored reward signal to improve learning efficiency. In addition, we explore various ways to adapt the discounting factor to the infra-step formulation. We investigate RL architectures based on Dueling Double Deep Q-learning, showing that the proposed RL-based EGCS adapts to fluctuating traffic patterns, learns from a highly stochastic environment, and thereby outperforms a traditional rule-based algorithm.

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Nathan Vaartjes, Vincent Francois-Lavet. 2025-06-12. Novel RL approach for efficient Elevator Group Control Systems. https://arxiv.org/abs/2507.00011

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