Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. However, acquiring these skills using only a stream of high-dimensional, unstructured, and unlabeled observations is a tricky challenge for any autonomous agent. Previous methods have used variational autoencoders to encode a scene into a low-dimensional vector that can be used as a goal for an agent to discover new skills. Nevertheless, in compositional/multi-object environments it is difficult to disentangle all the factors of variation into such a fixed-length representation of the whole scene. We propose to use object-centric representations as a modular and structured observation space, which is learned with a compositional generative world model. We show that the structure in the representations in combination with goal-conditioned attention policies helps the autonomous agent to discover and learn useful skills. These skills can be further combined to address compositional tasks like the manipulation of several different objects.

中文翻译：

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以对象为中心的自我监督视觉强化学习

自治代理需要大量的技能，才能合理地应对以前从未见过的新任务。但是，仅使用一系列高维，无结构和无标签的观察来获得这些技能对于任何自治代理来说都是棘手的挑战。先前的方法已使用变体自动编码器将场景编码为低维向量，可以将其用作代理发现新技能的目标。然而，在构图/多对象环境中，很难将所有变化因素分解为整个场景的固定长度表示形式。我们建议将以对象为中心的表示形式用作模块化和结构化的观察空间，并通过合成生成的世界模型来学习。我们表明，表示形式中的结构与目标条件注意策略相结合，可以帮助自治代理发现和学习有用的技能。这些技能可以进一步组合以解决构图任务，例如操纵几个不同的对象。