Structured World Models for Sample-Efficient Planning in Partially Known Dynamical Systems

Model-based decision making often involves partially known dynamical systems, where structural knowledge (e.g., stock-flow relations, conservation laws, delays, and mechanistic dependencies) is available while some dynamics remain unknown. Yet general-purpose world models often learn system dynamics largely from data without explicitly encoding this known structure. We propose a structured world-modeling framework that separates supplied structural knowledge from the dynamics that remain to be learned, exposes agent-controlled variables as actions, retains the state and memory needed for recursive multi-step prediction, and learns unknown parameters and, when needed, residual dynamics. The resulting transition model is not tied to a particular planner; in our discrete-action experiments, we primarily pair it with Monte Carlo tree search. In a realizable setting, we provide a finite-sample analysis showing how low-dimensional structural parameterization reduces system-identification complexity and how the resulting transition error propagates through multi-step rollouts to planning-value error and, under a sufficient action gap, preservation of the selected action. Experiments on system dynamics and control benchmarks, including larger-scale supply-chain and epidemic-intervention tasks, show strong planning performance under limited data and competitive performance as interaction budgets increase. These results suggest that explicitly preserving known system structure can provide a practical route to more sample-efficient model-based decision making.