[2511.14262] Object-Centric World Models for Causality-Aware Reinforcement Learning

Computer Science > Machine Learning arXiv:2511.14262 (cs) [Submitted on 18 Nov 2025 (v1), last revised 30 Mar 2026 (this version, v3)] Title:Object-Centric World Models for Causality-Aware Reinforcement Learning Authors:Yosuke Nishimoto, Takashi Matsubara View a PDF of the paper titled Object-Centric World Models for Causality-Aware Reinforcement Learning, by Yosuke Nishimoto and 1 other authors View PDF HTML (experimental) Abstract:World models have been developed to support sample-efficient deep reinforcement learning agents. However, it remains challenging for world models to accurately replicate environments that are high-dimensional, non-stationary, and composed of multiple objects with rich interactions since most world models learn holistic representations of all environmental components. By contrast, humans perceive the environment by decomposing it into discrete objects, facilitating efficient decision-making. Motivated by this insight, we propose \emph{Slot Transformer Imagination with CAusality-aware reinforcement learning} (STICA), a unified framework in which object-centric Transformers serve as the world model and causality-aware policy and value networks. STICA represents each observation as a set of object-centric tokens, together with tokens for the agent action and the resulting reward, enabling the world model to predict token-level dynamics and interactions. The policy and value networks then estimate token-level cause--effect relations and use them in ...