论文标题
通过图形神经网络学习以对象为中心的自动行为
Learning Object-Centered Autotelic Behaviors with Graph Neural Networks
论文作者
论文摘要
尽管人类生活在一个开放式的世界中，并且无休止地面临着新的挑战，但他们每次面对下一个挑战都不必从头开始学习。相反，他们可以使用一些以前学习的技能，这些技能迅速适应了新情况。在人工智能中，具有内在动机代表和设定自己的目标的自动特工具有有希望的技能适应能力。但是，这些功能受其政策和目标空间表示的高度限制。在本文中，我们建议研究这些表示对自动剂的学习和转移能力的影响。我们使用四种类型的图形神经网络策略表示和两种类型的目标空间（几何或基于谓词）研究自动剂的不同实现。通过对看不见的目标测试代理，我们表明，将足够表达的对象结构与语义关系目标相结合，有助于学习达到更加困难的目标。我们还发布了基于图的实现，以鼓励朝这个方向进行进一步的研究。
Although humans live in an open-ended world and endlessly face new challenges, they do not have to learn from scratch each time they face the next one. Rather, they have access to a handful of previously learned skills, which they rapidly adapt to new situations. In artificial intelligence, autotelic agents, which are intrinsically motivated to represent and set their own goals, exhibit promising skill adaptation capabilities. However, these capabilities are highly constrained by their policy and goal space representations. In this paper, we propose to investigate the impact of these representations on the learning and transfer capabilities of autotelic agents. We study different implementations of autotelic agents using four types of Graph Neural Networks policy representations and two types of goal spaces, either geometric or predicate-based. By testing agents on unseen goals, we show that combining object-centered architectures that are expressive enough with semantic relational goals helps learning to reach more difficult goals. We also release our graph-based implementations to encourage further research in this direction.
