基于强化学习的电梯系统模块化神经网络的构建演示

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ning Ning Chung , Hamed Taghavian , Mikael Johansson , Lock Yue Chew
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引用次数: 0

摘要

我们研究了神经网络如何完成通勤者从起点到目的地的电梯调度任务。与传统的神经网络应用方式不同,我们构建了一种特殊的神经网络架构,在考虑了领域知识和潜在未来行为的有效性后,优化了通勤者的出行时间。构建的体系结构是模块化的,具有神经元结构的构建块,服务于特定的功能角色。通过放松权重,然后通过强化学习训练该网络,我们证明它优于实现标准电梯算法的智能体。更值得注意的是,我们观察到在训练过程中经历的动作序列导致网络结构内功能模块的自发出现。神经网络的这种行为特征使其不像一个黑盒子,其功能的特定方面可以从其网络连接中明确地识别出来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A demonstration on the construction of modular neural network using elevator system that operates based on reinforcement learning
We study how neural networks can perform the task of elevator dispatching of commuters from their origins to their destinations. Instead of applying a neural network in the conventional way, we construct a specific neural network architecture that optimizes the commuters’ traveling time after taking into account the domain knowledge and the efficacy of potential future actions. The constructed architecture is modular with building blocks of neuronal structure that serve specified functional roles. By relaxing the weights and then training this network via reinforcement learning, we show that it outperforms an agent that implements the standard elevator algorithm. More remarkably, we observe the spontaneous emergence of functional modules within the structure of the network in consequence of the action sequences experienced during training. This behavioral feature of the neural network makes it less of a black box, with specific aspects of its functions being explicitly discernible from its network connections.
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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