Novel RL approach for efficient Elevator Group Control Systems
Nathan Vaartjes, Vincent Francois-Lavet
TL;DR
This paper presents a novel reinforcement learning approach for elevator group control systems that models the problem as a Markov Decision Process, introducing innovative encoding and reward strategies to outperform traditional methods.
Contribution
The paper introduces a new RL-based elevator control system with a unique action encoding, infra-steps for passenger modeling, and tailored rewards, advancing the state-of-the-art in elevator traffic management.
Findings
RL system adapts to fluctuating traffic patterns
Outperforms traditional rule-based algorithms
Effective in highly stochastic environments
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…
Peer Reviews
Decision·ICLR 2025 Conference Withdrawn Submission
- This feature, which models continuous passenger arrivals, creates a learning environment for the RL agent that mirrors real-life complexities. - The paper's approach is designed to avoid combinatorial complexity, ensuring efficient decision-making through a well-structured action space. This design choice provides a sense of relief about the model's efficiency. - The simulation design is based on the actual data set.
- This paper is still in the stage of considering the use of reinforcement learning, and the comparison with existing methods is insufficient.
The paper demonstrates significant strengths through its innovative approach to elevator dispatching using a novel reinforcement learning (RL) framework. By introducing infra-steps to simulate continuous passenger arrivals and formulating the problem as a Markov Decision Process (MDP), it effectively captures the complexities of elevator systems. The comprehensive comparison of fixed and variable discounting strategies, along with the exploration of branching and combinatorial RL architectures,
1. Experimental Comparison : The paper only compares the proposed method against the classical ETD algorithm. It does not include comparisons with recent RL-based approaches, making it difficult to evaluate the method's novelty and effectiveness in the broader RL research context. 2. Experimental Setup : The experiments are conducted using a single dataset, which limits the capacity to demonstrate the method's adaptability to diverse scenarios or environments. Testing across various conditions w
1. The authors focus on a very practical and meaningful real-world problem, which should be encouraged in the RL community. 2. The writing is very clear. Especially, the authors explained many definitions the elevator control very well. 3. The proposed new action space and infra-steps look simple but effective, which might benefit the empirical RL research very much. The significance is beyond the elevator group control.
1. The notations are sometimes confusing. For example, in equation (1) $G^\pi$ is a conditional expectation, which is not correct. $\pi$ is not a random variable. $\pi$ is a function and will change the state-action distribution. A common practice is to write $G$ as a function of $\pi$. 2. The contribution of infra-step is not very clear. The empirical results have shown that the fixed discounting works better than the variable discounting.
The topic of the paper is interesting. The paper is clearly written and easy to follow.
The contribution of the paper is minor in the sense that the details of the key elements proposed method are missing. For example the deep neural networks are not given. The other limitation is that the quality of the simulation model used for training the elevator group control algorithms is not clear.
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Taxonomy
TopicsElevator Systems and Control · Vibration and Dynamic Analysis · Traffic control and management