对于自主赛车政策的跳跃启动强化学习中的风险意识突变
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究引入了一种新的自主赛车控制框架,使用跳转启动强化学习 (RCM-JSRL) 和非线性模型预测控制 (NMPC) 的风险意识突变来提高速度和效率.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能和机器学习
背景情况:
- 自动赛车需要先进的轨迹规划和运动控制,以最大限度地提高车辆的性能.
- 现有的方法可能会在高风险,高复杂性的场景中扎,导致保守的政策.
研究的目的:
- 开发一个创新的自主赛车规划控制框架.
- 提高复杂,高风险的赛车环境中的培训效率和政策制定.
主要方法:
- 在跳转启动强化学习 (RCM-JSRL) 中整合风险意识突变与非线性模型预测控制 (NMPC).
- RCM-JSRL采用跳跃开始课程学习,风险意识的遗传算法,专家知识和好奇心驱动的探索.
- NMPC确保在本地最优的控制命令,尊重车辆动态和轨道约束.
主要成果:
- 拟议的控制器在接受了各种轨道图的培训后,与指导政策相比,表现出了优越的政策.
- 该框架有效平衡勘探和开采,避免过度保守的政策.
- 证明控制器在自主赛车中的有效性和可扩展性.
结论:
- 开发的RCM-JSRL和NMPC框架显著提高了自主赛车的性能.
- 潜在的应用包括提高日常驾驶的效率和稳定性,扩大自动驾驶能力.
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