Object-centric world models provide structured representation of the scene and can be an important backbone in reinforcement learning and planning. However, existing approaches suffer in partially-observable environments due to the lack of belief states. In this paper, we propose Structured World Belief, a model for learning and inference of object-centric belief states. Inferred by Sequential Monte Carlo (SMC), our belief states provide multiple object-centric scene hypotheses. To synergize the benefits of SMC particles with object representations, we also propose a new object-centric dynamics model that considers the inductive bias of object permanence. This enables tracking of object states even when they are invisible for a long time. To further facilitate object tracking in this regime, we allow our model to attend flexibly to any spatial location in the image which was restricted in previous models. In experiments, we show that object-centric belief provides a more accurate and robust performance for filtering and generation. Furthermore, we show the efficacy of structured world belief in improving the performance of reinforcement learning, planning and supervised reasoning.

这篇论文提出了一种名为“Structured World Belief”的模型，可用于学习和推理物体集中信念状态，通过SMC实现，提供多个物体集中场景的假设，以及一种新的物体集中动态模型，能够在长时间内跟踪不可见的物体状态，同时允许模型灵活关注图像中的任何空间位置，并显示其在强化学习、规划和监督推理中的有效性。

基于POMDP的强化学习结构化世界信念