基于改进DQN模型的目标区域消减算法

Target region reduction algorithm based on improved DQN model

针对智能设备进行火灾灭火或淤泥冲刷时,水流喷射方位控制算法低效问题,提出一种基于改进DQN模型的目标区域消减算法.首先,以改进的UNet网络为基础,结合提出的自相似池化与反池化运算方法,增强DQN模型提取环境图像中目标区域的能力.然后,利用ConvLSTM网络作为DQN模型的智能体,形成对过往环境和动作相关联的包含时间和空间维度的图像序列信息的有效记忆.最终本文算法实现对水流喷射方位的高效控制,其在测试集中进行仿真实验时的水流喷射次数相对其他四种对比算法的最优值降低12.1%.

This paper presents a target region reduction algorithm based on an improved DQN model to address the issue of inefficient control of water jet direction when utilizing intelligent devices for fire extinguishing or mud flushing.Firstly,by leveraging the enhanced UNet network and incorporating self-similarity pooling and anti-pooling methods,the DQN model significantly improves the extraction capability of the target region in the environmental image.Secondly,the ConvLSTM network is employed as the agent of the DQN model to generate effective memory associated with the past environments and actions which contains image sequence information encompassing both temporal and spatial dimensions.Ultimately,the algorithm described in this paper achieves efficient control of the water jet direction,resulting in a 12.1%reduction in the number of water jet instances during simulation experiments compared to the optimal values obtained from four other comparative algorithms.