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基于拍卖多智能体深度确定性策略梯度的多无人车分散策略研究

郭宏达 娄静涛 杨珍珍 徐友春

郭宏达, 娄静涛, 杨珍珍, 徐友春. 基于拍卖多智能体深度确定性策略梯度的多无人车分散策略研究[J]. 电子与信息学报, 2024, 46(1): 287-298. doi: 10.11999/JEIT221582
引用本文: 郭宏达, 娄静涛, 杨珍珍, 徐友春. 基于拍卖多智能体深度确定性策略梯度的多无人车分散策略研究[J]. 电子与信息学报, 2024, 46(1): 287-298. doi: 10.11999/JEIT221582
GUO Hongda, LOU Jingtao, YANG Zhenzhen, XU Youchun. Research on Dispersion Strategy for Multiple Unmanned Ground Vehicles Based on Auction Multi-agent Deep Deterministic Policy Gradient[J]. Journal of Electronics & Information Technology, 2024, 46(1): 287-298. doi: 10.11999/JEIT221582
Citation: GUO Hongda, LOU Jingtao, YANG Zhenzhen, XU Youchun. Research on Dispersion Strategy for Multiple Unmanned Ground Vehicles Based on Auction Multi-agent Deep Deterministic Policy Gradient[J]. Journal of Electronics & Information Technology, 2024, 46(1): 287-298. doi: 10.11999/JEIT221582

基于拍卖多智能体深度确定性策略梯度的多无人车分散策略研究

doi: 10.11999/JEIT221582
详细信息
    作者简介:

    郭宏达:男,博士生,研究方向为多无人车路径规划、车间通信等

    娄静涛:男,博士,工程师,研究方向为机器视觉、无人驾驶军民融合等

    杨珍珍:女,硕士,助教,研究方向为外军军事运输、美军航空运输等

    徐友春:男,博士生导师,教授,研究方向为无人系统架构、机器学习等

    通讯作者:

    娄静涛 loujt_1984@126.com

  • 中图分类号: TN911.7; T249

Research on Dispersion Strategy for Multiple Unmanned Ground Vehicles Based on Auction Multi-agent Deep Deterministic Policy Gradient

  • 摘要: 多无人车(multi-UGV)分散在军事作战任务中应用非常广泛,现有方法较为复杂,规划时间较长,且适用性不强。针对此问题,该文提出一种基于拍卖多智能体深度确定性策略梯度(AU-MADDPG)算法的多无人车分散策略。在单无人车模型的基础上,建立基于深度强化学习的多无人车分散模型。对MADDPG结构进行优化,采用拍卖算法计算总路径最短时各无人车所对应的分散点,降低分散点分配的随机性,结合MADDPG算法规划路径,提高训练效率及运行效率;优化奖励函数,考虑训练过程中及结束两个阶段,全面考虑约束,将多约束问题转化为奖励函数设计问题,实现奖励函数最大化。仿真结果表明:与传统MADDPG算法相比,所提算法在训练时间上缩短了3.96%,路径总长度减少14.50%,解决分散问题时更为有效,可作为此类问题的通用解决方案。
  • 图  1  多无人车分散场景示意图

    图  2  无人车运动学模型

    图  3  多无人车分散训练测试框架

    图  4  多无人车分散算法流程图

    图  5  训练环境

    图  6  分散环境示意图

    7  不同算法下多无人车分散轨迹

    图  8  算法平均奖励

    图  9  训练过程中耗时

    图  10  测试过程中耗时

    图  11  基于遗传算法的多无人车分散路径

    表  1  AU-MADDPG算法参数设置

    参数
    经验池大小M1000
    actor学习率la0.01
    critic学习率lc0.01
    最小批学习数N32
    迭代总次数E105
    每次迭代的最大步数T25
    网络更新率τ0.01
    运行采样时间δt (s)0.1
    下载: 导出CSV

    表  2  测试100次路径长度对比

    MADQNMADDPGAU-MADDPG
    无障碍环境总长度545.272240.915205.959
    最长路径13.2845.4825.618
    最短路径1.8830.1380.220
    越野
    环境
    总长度602.498285.717258.436
    最长路径13.8755.8365.661
    最短路径2.1320.2990.282
    城市
    环境
    总长度692.331346.120288.96
    最长路径15.0627.0536.915
    最短路径2.1250.5260.469
    下载: 导出CSV

    表  3  算法耗时对比

    MADQNMADDPGAU-MADDPG
    无障碍
    环境
    训练迭代40000次(s)14548.7098417.0648083.574
    测试100次(s)45.6266.4416.324
    越野
    环境
    训练迭代40000次(s)15129.6618852.9188366.883
    测试100次(s)50.7707.3976.935
    城市
    环境
    训练迭代40000次(s)14998.7588997.2018401.293
    测试100次(s)51.2157.4597.061
    下载: 导出CSV

    表  4  MADDPG单方面优化性能

    优化奖励函数引入拍卖算法
    训练迭代40000次耗时(s)8151.7018305.227
    平均奖励–310.411–382.506
    测试100次耗时(s)6.3536.409
    总长度235.881210.680
    下载: 导出CSV

    表  5  遗传算法测试结果

    迭代次数耗时(s)总路径最短路径最长路径
    10064.323395.6700.35210.557
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-01-02
  • 修回日期:  2023-05-12
  • 网络出版日期:  2023-05-22
  • 刊出日期:  2024-01-17

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