  Causal dynamics models (CDMs) have demonstrated significant potential in
addressing various challenges in reinforcement learning. To learn CDMs, recent
studies have performed causal discovery to capture the causal dependencies
among environmental variables. However, the learning of CDMs is still confined
to small-scale environments due to computational complexity and sample
efficiency constraints. This paper aims to extend CDMs to large-scale
object-oriented environments, which consist of a multitude of objects
classified into different categories. We introduce the Object-Oriented CDM
(OOCDM) that shares causalities and parameters among objects belonging to the
same class. Furthermore, we propose a learning method for OOCDM that enables it
to adapt to a varying number of objects. Experiments on large-scale tasks
indicate that OOCDM outperforms existing CDMs in terms of causal discovery,
prediction accuracy, generalization, and computational efficiency.
