论文标题
因果世界模型的内在动机学习
Intrinsically Motivated Learning of Causal World Models
论文作者
论文摘要
尽管在深度学习和强化学习方面取得了进展，但与人类（或动物）智力相比，在特定任务上学习的技能的转移和推广非常有限。终身，逐步建立常识知识可能是实现更一般智力的方式的必要组成部分。一个有希望的方向是建立世界模型，以捕获与环境相互作用后面隐藏的真正物理机制。在这里，我们探讨了这样一种观念，即推断环境的因果结构可以受益于精心挑选的行动，作为收集相关介入数据的手段。
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.
