# Application of Deep Q Learning with Simulation Results for Elevator Optimization

arXiv: https://arxiv.org/abs/2210.00065 · HTML: https://mlsanity.com/paper/2210.00065/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 30 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [An Elementary Proof that Q-learning Converges Almost Surely](https://mlsanity.com/paper/2108.02827/) (2021)
- [Application of Convolutional Neural Networks with Quasi-Reversibility Method Results for Option Forecasting](https://mlsanity.com/paper/2208.14385/) (2022)
- [Elevator Optimization: Application of Spatial Process and Gibbs Random Field Approaches for Dumbwaiter Modeling and Multi-Dumbwaiter Systems](https://mlsanity.com/paper/2209.12401/) (2022)

## Abstract (arXiv)

This paper presents a methodology for combining programming and mathematics to optimize elevator wait times. Based on simulated user data generated according to the canonical three-peak model of elevator traffic, we first develop a naive model from an intuitive understanding of the logic behind elevators. We take into consideration a general array of features including capacity, acceleration, and maximum wait time thresholds to adequately model realistic circumstances. Using the same evaluation framework, we proceed to develop a Deep Q Learning model in an attempt to match the hard-coded naive approach for elevator control. Throughout the majority of the paper, we work under a Markov Decision Process (MDP) schema, but later explore how the assumption fails to characterize the highly stochastic overall Elevator Group Control System (EGCS).