One Life to Learn: Inferring Symbolic World Models for Stochastic
  Environments from Unguided Exploration
Authors: name: Zaid Khan; name: Archiki Prasad; name: Elias Stengel-Eskin; name: Jaemin Cho; name: Mohit Bansal
Published: 2025-10-14 02:49:32

Abstract: Symbolic world modeling requires inferring and representing an environment's
transitional dynamics as an executable program. Prior work has focused on
largely deterministic environments with abundant interaction data, simple
mechanics, and human guidance. We address a more realistic and challenging
setting, learning in a complex, stochastic environment where the agent has only
"one life" to explore a hostile environment without human guidance. 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,
activating in relevant world states. This creates a dynamic computation graph
that routes inference and optimization only through relevant laws, avoiding
scaling challenges when all laws contribute to predictions about a complex,
hierarchical state, and enabling the learning of stochastic dynamics even with
sparse rule activation. 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 Crafter environment that exposes a structured,
object-oriented symbolic state 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 test OneLife's planning ability, with simulated
rollouts successfully identifying superior strategies. Our work establishes a
foundation for autonomously constructing programmatic world models of unknown,
complex environments.