对连接和自动化车辆的多代理增强学习进行全面的调查.
Pamul Yadav1, Ashutosh Mishra1, Shiho Kim1
1School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本调查探讨了连接和自动化车辆 (CAV) 的多代理强化学习 (MARL). 它确定了复杂的交通管理任务的当前挑战和未来研究方向.
科学领域:
- 智能运输系统 智能运输系统
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 连接和自动化车辆 (CAV) 需要复杂的,同时的任务管理,包括运动规划和交通控制.
- 多代理强化学习 (MARL) 为解决这些复杂的控制问题提供了一个有希望的框架.
- 现有的研究缺乏对CAV中MARL应用的全面概述,这阻碍了进一步的发展.
研究的目的:
- 为连接和自动化车辆 (CAV) 中的多代理强化学习 (MARL) 应用提供全面的调查.
- 分析当前的研究趋势,确定关键挑战,并提出未来的研究方向在MARL为CAVs.
主要方法:
- 基于对CAV的MARL现有研究论文的分类分析.
- 识别和分类当前的发展和研究方向.
- 讨论挑战和潜在的未来研究途径.
主要成果:
- 该调查对CAV的当前MARL研究进行了分类,突出了各种方法和应用.
- 在复杂的CAV任务中实施MARL的关键挑战被确定.
- 提出了潜在的未来研究领域,以应对已确定的挑战.
结论:
- 马尔 (MARL) 是一项关键技术,可以在CAV中实现复杂的功能.
- 需要进一步的研究来克服当前的挑战,并充分发挥MARL在智能交通系统中的潜力.
- 这项调查对研究人员和从业人员在CAV的MARL领域提供了宝贵的资源.
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