可扩展的基于MADDPG的合作目标入侵,用于多USV系统
IEEE transactions on neural networks and learning systems
|September 7, 2023
概括
一种新的可扩展的深度强化学习 (DRL) 方法使多个无人地面车辆 (多个USV) 能够合作入侵目标. 这种方法允许在复杂的海上任务培训期间灵活扩展多USV系统.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 海洋工程 海洋工程
背景情况:
- 通过多个无人地面车辆 (多个USV) 进行合作的目标入侵,带来了可扩展性的挑战.
- 在培训期间,代理人数量的动态变化可能导致政策不稳定.
研究的目的:
- 提出一种新的可扩展的深度强化学习 (DRL) 方法,用于多个USV的合作目标入侵.
- 针对多USV系统的DRL,解决政策波动,增强勘探开发平衡.
主要方法:
- 介绍了可扩展-MADDPG,一个强化学习 (RL) 算法,允许实时系统扩展.
- 整合双向长期短期记忆 (Bi-LSTM) 网络以稳定政策.
- 实施一个改进的epsilon-greedy战略与奥恩斯坦-乌伦贝克 (OU) 噪音,以实现强大的政策优化.
主要成果:
- 拟议的可扩展-MADDPG方法证明了多USV系统的有效合作目标入侵.
- 双LSTM网络成功地减轻了培训期间的政策波动.
- 加强的勘探战略提高了学习政策的稳定性.
结论:
- 可扩展-MADDPG为可扩展的多USV合作控制提供了灵活和有效的解决方案.
- 整合Bi-LSTM和改进的勘探战略提高了DRL在复杂的海洋环境中的性能.
- 该方法通过实验结果得到验证,表明其实际适用性.
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