基于深度强化学习的多出口疏散仿真
Simulating multi-exit evacuation using deep reinforcement learning
摘要：
传统的多出口室内疏散模拟主要集中在如何确定一个合理的出口的基础上，在不断变化的环境中的众多因素。结果通常包括一些拥挤和其他未充分利用的出口，特别是大量行人。本文提出了一种基于深度强化学习（DRL）的多出口疏散模拟方法，简称MultiExit-DRL，该方法采用深度神经网络（DNN）框架来实现状态到动作的映射。DNN框架应用Rainbow Deep Q-Network（DQN），这是一种DRL算法，集成了多种先进的DQN方法，以提高数据利用率和算法稳定性，并进一步将行动空间划分为八个等距方向，以供行人选择。我们比较了MultiExit-DRL与两个传统的多出口疏散模拟模型在三个不同的场景：1）不同的行人分布比例，2）不同的出口宽度比，3）不同的开放时间表的出口。实验结果表明，MultiExit-DRL算法在减少疏散帧总数的同时，具有较高的学习效率。此外，DRL的整合允许行人探索其他潜在的出口，并帮助确定最佳方向，从而提高出口利用率。
英文摘要：
Conventional simulations on multi-exit indoor evacuation focus primarily on how to determine a reasonable exit based on numerous factors in a changing environment. Results commonly include some congested and other under-utilized exits, especially with massive pedestrians. We propose a multi-exit evacuation simulation based on Deep Reinforcement Learning (DRL), referred to as the MultiExit-DRL, which involves in a Deep Neural Network (DNN) framework to facilitate state-to-action mapping. The DNN framework applies Rainbow Deep Q-Network (DQN), a DRL algorithm that integrates several advanced DQN methods, to improve data utilization and algorithm stability, and further divides the action space into eight isometric directions for possible pedestrian choices. We compare MultiExit-DRL with two conventional multi-exit evacuation simulation models in three separate scenarios: 1) varying pedestrian distribution ratios, 2) varying exit width ratios, and 3) varying open schedules for an exit. The results show that MultiExit-DRL presents great learning efficiency while reducing the total number of evacuation frames in all designed experiments. In addition, the integration of DRL allows pedestrians to explore other potential exits and helps determine optimal directions, leading to the high efficiency of exit utilization.
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