HG2P:受海马体启发的高奖励图表和无模型的Q梯度惩罚,用于路径规划和运动控制
Haoran Wang1, Yaoru Sun1, Zeshen Tang1
1Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China.
概括
这项研究介绍了HG2P+ACLG,这是一个由大脑机制启发的新型目标条件下等级强化学习 (HRL) 框架. 它提高了复杂的长视野任务的样本效率和概括性,例如导航和机器人操纵.
科学领域:
- 人工智能的人工智能
- 神经科学是一个神经科学.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 以目标为条件的层次增强学习 (HRL) 有效地将复杂的任务分解为可管理的子目标.
- 现有的HRL方法在长期规划和在大规模环境中的泛化方面面临挑战.
- 将HRL与大脑机制相结合,有可能改善学习策略.
研究的目的:
- 提出一个新的目标条件的HRL框架,HG2P+ACLG,将基于图表的规划与大脑启发的机制集成在一起.
- 通过结合高回报采样和无模型梯度处罚来提高HRL中的采样效率和概括性.
- 验证关于复杂的远程导航和机器人操纵任务的拟议框架.
主要方法:
- 开发了一种类似于海马-状体的双控制器假设,用于目标条件的HRL.
- 介绍了一种高回报率采样策略用于内存图形构建,灵感来自海马重复和基于实例的理论.
- 导出了一个无模型的低级Q函数梯度惩罚,以解决模型依赖性和改进Lipschitz约束概括.
- 将这些组件集成到ACLG框架中,创建HG2P+ACLG.
主要成果:
- 与最先进的目标条件HRL算法相比,HG2P+ACLG框架显示出更高的性能.
- 在各种长视界导航任务中观察到显著的改进.
- 在复杂的机器人操纵任务中,性能提升也很明显.
结论:
- 拟议的HG2P+ACLG框架为目标条件的HRL提供了有前途的进展.
- 脑启发机制和新型算法组件的整合导致更好的样本效率和概括性.
- 这种方法有效地解决了复杂的现实应用的长期规划方面的挑战.
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