LG-H-PPO:在隐藏图上进行机器人路径规划的离线分层PPO
1China University of Petroleum (East China), Qingdao, China.
Frontiers in robotics and AI
|January 23, 2026
概括
本研究介绍了基于隐形图的等级近接策略优化 (LG-H-PPO),这是一种用于机器人路径规划的新方法. 通过使用潜图,它简化了复杂的决策,提高了培训效率和在具有挑战性的导航任务中取得成功率.
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
- 机器学习 机器学习
背景情况:
- 在复杂的环境中自主机器人路径规划至关重要,但长期决策和稀少的奖励给它带来了挑战.
- 传统的强化学习 (RL) 与这些问题作斗争,而离线层次式RL (HRL) 分解任务,但面临着连续潜伏空间的子目标的困难.
- 现有的先进的离线HRL方法,如Guider和HIQL使用隐性空间,但遭受复杂的,样本效率低下的政策优化,导致不稳定的培训.
研究的目的:
- 提出一种新的离线等级强化学习框架,即基于隐形图的等级近接政策优化 (LG-H-PPO),以解决高层政策中持续隐藏空间探索的局限性.
- 通过简化高层政策的行动空间,提高离线HRL算法的训练效率和稳定性,用于长序列导航任务.
主要方法:
- 引入了一个名为LG-H-PPO的新型离线层级PPO框架.
- 将连续潜伏空间分离化为结构化的"潜伏图",以将高层规划从连续创建转变为分离选择.
- 在标准D4RL线下导航基准上对LG-H-PPO进行评估.
主要成果:
- 在收速度和最终任务成功率方面,LG-H-PPO在先进基线 (指南,HIQL) 上表现出显著的优势.
- 隐藏空间的 дискрети化大大降低了高层政策的学习困难.
- 预先的实验证实了在标准基准上提出的方法的有效性.
结论:
- 将图形结构集成到潜变量HRL规划中简化了高级政策的行动空间,提高了培训效率和稳定性.
- 对于未来的离线HRL研究,LG-H-PPO提供了一个有希望的方向,特别是对于长序导航任务.
- 这项工作为将隐性变量表示与离线HRL中的显式图形规划相结合奠定了基础.
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