Despite the recent progress in deep learning and reinforcement learning, transfer and generalization of skills learned on specific tasks is very limited compared to human (or animal) intelligence. The lifelong, incremental building of common sense knowledge might be a necessary component on the way to achieve more general intelligence. A promising direction is to build world models capturing the true physical mechanisms hidden behind the sensorimotor interaction with the environment. Here we explore the idea that inferring the causal structure of the environment could benefit from well-chosen actions as means to collect relevant interventional data.
翻译:尽管最近在深层学习和加强学习、转让和普及具体任务所学到的技能方面取得了进展,但与人类(或动物)的智力相比,这些技能的传授和普及非常有限。 终生、逐步建立常识知识可能是实现更一般的智慧的必要组成部分。一个有希望的方向是建立世界模型,捕捉隐藏在感官与环境中相互作用背后的真正物理机制。在这里,我们探讨这样一种想法,即推断环境的因果结构可以受益于精心选择的行动,作为收集相关干预数据的手段。