基于协作式深度强化学习的火灾应急疏散仿真研究

火灾是威胁公共安全的主要灾害之一，火灾产生的高温和有毒有害烟气严重影响了疏散路径的选择。将深度强化学习引入到应急疏散仿真研究，针对多智能体环境提出了协作式双深度Q网络算法。建立随时间动态变化的火灾场景模型，为人员疏散提供实时的危险区域分布信息；对各自独立的智能体神经网络进行整合，建立多智能体统一的深度神经网络，实现所有智能体之间的神经网络和经验共享，提高整体协作疏散效率。结果表明：所提方法具有良好的稳定性和适应性，训练和学习效率得到提升，具有良好的应用价值。

Research on Fire Emergency Evacuation Simulation Based on Cooperative Deep Reinforcement Learning

The fire accident is a major threat to the public safety, in which the high temperature, toxic and harmful gases seriously interfer the selection of the evacuation routes. Deep reinforcement learning is introduced into the research of emergency evacuation simulation, and a cooperative double deep Q network algorithm is proposed for the multi-agent environment. A fire scene model that changes dynamically over time is established to provide the real-time information on the distribution of the dangerous areas for the evacuation. The independent agent neural networks are integrated and the multi-agent unified deep neural network is established to realize the sharing of the neural network and experience among all agents, and improve the overall cooperative evacuation efficiency. The experimental comparison results show that the proposed method has the good stability and adaptability, improved training and learning efficiency, and good application value.