基于矢量导航使用人工代理的网格式表示
Andrea Banino1,2,3, Caswell Barry4, Benigno Uria5
1DeepMind, London, UK. abanino@google.com.
Nature
|May 11, 2018
概括
研究人员开发了一种深度强化学习代理,它使用类似网格的神经表征,灵感来自哺乳动物的大脑,在复杂的环境中实现专家级导航. 这种方法增强了人工智能的空间认知和规划能力.
科学领域:
- 神经科学
- 人工智能
- 计算神经科学
背景情况:
- 深度神经网络在许多任务中表现出色,
- 哺乳动物的导航依赖于内腔皮层的网格细胞进行空间表示和路径整合.
- 目前的人工智能缺乏哺乳动物的复杂空间认知能力.
研究的目的:
- 通过利用网格细胞功能开发具有哺乳动物类导航能力的深度强化学习代理.
- 调查类似网格的表示是否可以在具有挑战性的环境中提高代理的性能.
- 探索新兴的导航网状表示的计算效益.
主要方法:
- 训练一个循环神经网络来执行路径集成,观察类似网格的表示的出现.
- 作为深度强化学习导航代理的基础.
- 在不熟悉和变化的环境中对专家人类和比较剂进行评估.
主要成果:
- 循环网络的表现类似于网格细胞和其他内腔细胞.
- 在导航任务中,具有网格形态的代理人显著超过了人类专家和其他代理人.
- 新兴的网格式单元为基于矢量导航提供了度量,并启用了快捷方式行为.
结论:
- 新兴的网格式表示为代理提供欧几里德空间度量和向量运算,这对于熟练的导航至关重要.
- 这种方法支持神经科学理论在基于矢量导航的网格细胞的作用.
- 结合基于路径和基于矢量的策略,使用类似网格的表示,可以在复杂的动态环境中增强导航.
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