One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration

Symbolic world modeling is the task of inferring and representing the transitional dynamics of an environment as an executable program. Previous research on symbolic world modeling has focused on simple, deterministic environments with abundant data and human-provided guidance. We address the more realistic and challenging problem of learning a symbolic world model in a complex, stochastic environment with severe constraints: a limited interaction budget where the agent has only “one life” to explore a hostile environment and no external guidance in the form of human-provided, environment-specific rewards or goals. We introduce OneLife, a framework that models world dynamics through conditionally-activated programmatic laws within a probabilistic programming framework. Each law operates through a precondition-effect structure, allowing it to remain silent on irrelevant aspects of the world state and predict only the attributes it directly governs. This creates a dynamic computation graph that routes both inference and optimization only through relevant laws for each transition, avoiding the scaling challenges that arise when all laws must contribute to predictions about a complex, hierarchical state space, and enabling accurate learning of stochastic dynamics even when most rules are inactive at any given moment. To evaluate our approach under these demanding constraints, we introduce a new evaluation protocol that measures (a) state ranking, the ability to distinguish plausible future states from implausible ones, and (b) state fidelity, the ability to generate future states that closely resemble reality. We develop and evaluate our framework on Crafter-OO, our reimplementation of the popular Crafter environment that exposes a structured, object-oriented symbolic state and and a pure transition function that operates on that state alone. OneLife can successfully learn key environment dynamics from minimal, unguided interaction, outperforming a strong baseline on 16 out of 23 scenarios tested. We also demonstrate the world model’s utility for planning, where rollouts simulated within the world model successfully identify superior strategies in multi-step goal-oriented tasks. Our work establishes a foundation for autonomously constructing programmatic world models of unknown, complex environments.