具有杀戮链信息的多目标优化等级增强学习,以提高自主无人群群的弹性
Yingdong Gou1, Siwen Wei2, Kai Xu1
1Northwest Institute of Mechanical and Electrical Engineering, Xianyang, 712099, Shaanxi, China.
概括
本研究介绍了HRL-KCIMOO,这是一个新的层次强化学习框架. 它通过整合知识预训练和动态多目标优化来提高自主无人机群 (AUS) 对敌对攻击的弹性,以提高任务成功.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 多代理系统 多代理系统
- 强化学习是一种强化学习.
背景情况:
- 自主无人机群 (AUS) 需要对抗敌对干扰的弹性,才能成功执行任务.
- 当前的方法在复杂的多代理系统中与一般化和融合作斗争.
- 在AUS中单一目标的强化学习 (RL) 隐藏了权衡,限制了适应性反应.
研究的目的:
- 制定一个强有力的框架,以提高澳大利亚对抗威胁的弹性.
- 通过整合知识和多目标优化来克服传统RL的局限性.
- 提高在动态压力下AUS的适应性反应能力.
主要方法:
- 拟议的HRL-KCIMOO:一个层次化的强化学习框架.
- 使用了预先训练有反差学习,拓结构重建和中心意识排名的图表注意力编码器.
- 采用LSTM实现了高级参与者-关键架构,用于适应性目标权重和分散的低级代理执行.
主要成果:
- 在各种对抗性环境中,HRL-KCIMOO表现优于既定基准.
- 该框架维持了显著更高的任务成功率,即使在严重的群体消耗下.
- 实现了目标的动态调和:快速恢复性,运营连续性和系统稳定性.
结论:
- HRL-KCIMOO有效地提高了AUS的弹性和对抗敌对干扰的任务成功.
- 知识预训练和动态多目标优化的整合对于适应性群体行为至关重要.
- 这种方法为在具有挑战性的环境中强大的自主系统提供了显著的进步.
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