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

Understand

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.