在基于强化学习的自动无人机导航系统中实现知识的可移植性
José M Barreiro1, Juan A Lara2, Daniel Manrique1
1Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain.
PeerJ. Computer science
|June 22, 2023
概括
人工智能 (AI) 使无人驾驶汽车能够在代理人之间传输导航知识,改善新环境中的任务性能. 这种人工智能的进步提高了网络物理系统 (CPS) 的可靠性和自主性.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 网络物理系统 网络物理系统
背景情况:
- 知识转移是人工智能 (AI) 的一个关键挑战,它限制了AI模型在各种任务和环境中的适用性.
- 网络物理系统 (CPS) 需要提高可靠性和自主性,往往需要适应性AI解决方案.
- 在CPS中,无人驾驶车辆 (无人机) 在充满障碍的动态环境中面临复杂的导航挑战.
研究的目的:
- 开发和评估能够进行知识转移的无人驾驶汽车的强化学习系统.
- 通过利用其他代理商的学习知识,在未知的环境中实现无人机的自主导航.
- 量化知识可移植性在提高导航成功率和减少学习时间方面的有效性.
主要方法:
- 实施强化学习系统,为无人驾驶汽车 (无人机) 提供自主导航.
- 开发一种方法来隔离和转移人工智能代理之间的学习导航知识.
- 在具有物理障碍的各种模拟环境中对知识传输系统的实验验证.
主要成果:
- 拟议的系统证明了成功的知识隔离和代理人之间的知识转移.
- 使用转移知识的代理人在导航未知的环境中取得了更高的成功率.
- 与基线方法相比,在使用转移知识的代理人中观察到学习时间的显著减少.
- 该系统在78.3%的测试中表现优于基线,显示出更好的成功率和更短的学习时间.
结论:
- 开发的强化学习系统有效地实现了无人驾驶汽车在网络物理系统中的知识可移植性.
- 知识转移显著提高了无人机在复杂,未知的环境中导航的自主性和可靠性.
- 这种方法为改善现实应用中的AI适应性和效率提供了有希望的解决方案.
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