In this paper, we propose a novel approach, COMPAS (COMPose Actions and Slots), which leverages the strengths of state-of-the-art object-centric approaches for modeling the dynamics of an environment. Our method encodes the environment’s state into symbol-like, object-centric representations, known as slots, where each slot corresponds to an individual object. This approach offers a structured and interpretable way to model complex environments by combining slots with action representations for accurate next-state prediction. The primary contribution of our work is an efficient world model with a dynamics predictor capable of predicting accurate trajectories in action-dependent environments. Additionally, our slot extractor module enhances the predictive capabilities by extracting deterministic slots that remain consistent both within a single trajectory and across episodes. Unlike slots sampled from a trainable distribution, deterministic slots are generated from a single trainable parameter together with slot positional embeddings. This design improves the consistency across episodes, which in turn leads to more accurate dynamics prediction. We present a comprehensive evaluation of our approach in various environments, demonstrating that our proposed method outperforms competing models in environments with discrete and continuous action spaces.
Vorobyov et al. (Wed,) studied this question.