We study the problem of binding actions to objects in object-factored world models using action-attention mechanisms. We propose two attention mechanisms for binding actions to objects, soft attention and hard attention, which we evaluate in the context of structured world models for five environments. Our experiments show that hard attention helps contrastively-trained structured world models to learn to separate individual objects in an object-based grid-world environment. Further, we show that soft attention increases performance of factored world models trained on a robotic manipulation task. The learned action attention weights can be used to interpret the factored world model as the attention focuses on the manipulated object in the environment.
翻译:我们利用行动注意机制研究对受物体影响的世界模型中的物体采取约束性行动的问题,我们提出对物体采取约束性行动、软注意力和硬注意力的两个注意机制,我们根据五个环境的结构化世界模型对这些机制进行评价,我们的实验表明,硬注意力有助于由不同程度的训练结构化世界模型学会在以物体为基础的网络-世界环境中将个别物体分开。此外,我们还表明,软注意力提高了在机器人操纵任务方面受过训练的具有要素性的世界模型的性能。所学到的行动注意权重可以用来解释以因素化世界模型,因为注意力集中在环境中被操纵的物体上。