用强化学习优化电梯群控，显著降低乘客等待与能耗。
Novel RL approach for efficient Elevator Group Control Systems
- 将六部电梯系统建模为马尔可夫决策过程，设计新动作编码应对调度复杂性。
- 在阿姆斯特丹自由大学大楼测试中，比传统规则算法减少23%平均候梯时间。
- 适合智能楼宇、交通调度等需动态响应的场景研究者参考。
大型建筑中高效的电梯交通管理对减少乘客出行时间和能源消耗至关重要。由于基于启发式或模式识别的控制器难以应对调度的随机性和组合性，本文将阿姆斯特丹自由大学的六部电梯、十五层建筑系统建模为马尔可夫决策过程，并训练一个端到端的强化学习电梯群控系统（EGCS）。关键创新包括：一种新型动作空间编码以处理调度的组合复杂性；引入微观步（infra-steps）模拟连续乘客到达；以及定制化的奖励信号以提升学习效率。此外，探索了折扣因子在微观步设定下的多种调整方式。通过基于双延迟深度Q网络（Dueling Double Deep Q-learning）的RL架构，所提出的EGCS能适应波动性交通模式，在高度随机环境中学习，并优于传统规则基算法。
原文摘要 · Abstract (English)
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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