学习自主赛车驾驶与动作映射强化学习加强学习
Yuanda Wang1, Xin Yuan1, Changyin Sun2
1School of Automation, Southeast University, Nanjing 210096, China.
ISA transactions
|May 23, 2024
概括
本研究介绍了一种动作映射强化学习 (AM-RL) 方法,用于自动赛车控制. 这种新的方法提高了性能,并在不同的摩擦条件下概括了驾驶政策.
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
- 控制系统 控制系统
背景情况:
- 自动赛车驾驶要求在物理限制下运行车辆,以获得最佳性能.
- 有限的轮胎-道路摩擦引入了复杂的状态依赖的输入约束.
- 现有的强化学习 (RL) 方法难以应对这些动态约束.
研究的目的:
- 为自动赛车驾驶开发一种新的强化学习 (RL) 方法.
- 为了解决由轮胎与道路摩擦所造成的依赖于状态的输入约束.
- 提高学习驾驶政策的概括能力.
主要方法:
- 一个新的行动映射 (AM) 机制被整合到强化学习 (RL) 框架中.
- 开发了一种数值近似方法来实现AM机制,处理复杂的摩擦动态.
- 在一个定制的赛车模拟器中评估了AM-RL方法.
主要成果:
- 与传统的RL方法相比,建议的AM-RL方法显著减少了圈速.
- 通过AM-RL方法实现了更高的成功率.
- 实验验证证了驾驶政策在不同摩擦条件下的概括能力.
结论:
- 该AM-RL方法有效地管理自动赛车驾驶的状态依赖的输入约束.
- 这种方法为不同的摩擦条件提供了更好的性能和强度.
- 此外,AM机制在具有挑战性的控制场景中增强了RL的实际应用性.
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