通过先进的PER-TD3框架,通过自主保持车道的自适应性增强
Xiting Peng1,2,3, Jinyan Liang4, Xiaoyu Zhang5
1Shenyang Fengchi Software Co., Ltd, Shenyang, China.
Frontiers in artificial intelligence
|November 3, 2025
概括
本研究介绍了双延迟深确定性政策梯度 (PER-TD3) 的优先经验重复,以改善自动驾驶车辆的车道保持. 该方法提高了训练效率和算法融合,以实现更安全的驾驶.
科学领域:
- 自主驾驶系统 自主驾驶系统
- 机器学习用于机器人技术
- 控制理论 控制理论
背景情况:
- 有效和安全的车道保持对于自动驾驶技术至关重要.
- 目前的深度强化学习 (DRL) 方法面临的挑战包括培训效率低,车道维护任务的趋同缓慢等.
研究的目的:
- 提高自动驾驶汽车的学习效率和保持车道性能.
- 为了解决现有的DRL方法在车道保持应用中的局限性.
主要方法:
- 一个优先级经验重复 (PER) 机制适用于双延迟深度决定性政策梯度 (TD3) 算法,命名为PER-TD3.
- 根据预测和真实Q值之间的差异来优先考虑样本,以改善决策.
- 使用概率抽样机制来增强样本多样性和高价值体验回放.
主要成果:
- PER-TD3框架有效地解决了在DRL中常见的不平衡培训问题.
- 观察到增强的培训样本质量和加速的算法融合.
- 在TORCS平台上显示了驾驶性能和安全性的显著改善.
结论:
- 拟议的PER-TD3方法显著提高了自动驾驶汽车的车道保持性能.
- 这种方法导致更准确,更稳定,更有效地学习驾驶策略.
- PER-TD3为提高自动驾驶系统的安全性和效率提供了一个有前途的解决方案.
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