One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
OneLife framework learns symbolic world models in Crafter-OO via unguided exploration, outperforming baselines.
Key Findings
Methodology
OneLife framework 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, forming a dynamic computation graph for inference and optimization.
Key Results
- In Crafter-OO, OneLife outperformed strong baselines in 16 out of 23 scenarios, demonstrating its ability to learn stochastic dynamics with sparse rule activation.
- OneLife excelled in state ranking and state fidelity evaluation protocols, distinguishing plausible future states and generating realistic future states.
- Through simulated rollouts, OneLife successfully identified superior strategies in multi-step goal-oriented tasks.
Significance
This research establishes a foundation for autonomously constructing programmatic world models in unknown, complex environments, addressing long-standing challenges in symbolic world modeling, particularly in unguided and sparse data scenarios.
Technical Contribution
OneLife framework avoids scalability challenges by activating only relevant laws, providing new theoretical guarantees and engineering possibilities, significantly differing from existing methods.
Novelty
OneLife is the first framework to learn symbolic world models through a single unguided exploration in complex stochastic environments, overcoming limitations of prior work in simpler deterministic settings.
Limitations
- OneLife may struggle in extremely sparse environments due to low rule activation frequency, hindering learning.
- In complex interaction scenarios, the combination of laws may increase computational complexity.
Future Work
Future research can explore improving law generation and optimization efficiency in more complex environments and extending to more types of stochastic dynamics.
AI Executive Summary
Symbolic world modeling is a critical task in artificial intelligence, providing agents with a functional understanding of environmental dynamics. Existing research has focused on simple deterministic environments, relying on abundant interaction data and human guidance. However, real-world environments are often complex and stochastic, posing significant challenges for agents during exploration.
The OneLife framework models world dynamics through conditionally-activated programmatic laws within a probabilistic programming framework, learning key environment dynamics from minimal unguided interaction. It outperformed strong baselines in the Crafter-OO environment, demonstrating its ability to learn stochastic dynamics with sparse rule activation.
This research establishes a foundation for autonomously constructing programmatic world models in unknown, complex environments, addressing long-standing challenges in symbolic world modeling, particularly in unguided and sparse data scenarios. Future research can explore improving law generation and optimization efficiency in more complex environments and extending to more types of stochastic dynamics.
Deep Analysis
Background
Symbolic world modeling is an important research direction in AI, aiming to infer and represent environmental transition dynamics as executable programs. Existing research has focused on simple deterministic environments, relying on abundant interaction data and human guidance. However, real-world environments are often complex and stochastic, posing significant challenges for agents during exploration.
Core Problem
Learning symbolic world models in complex stochastic environments poses significant challenges, especially in unguided and sparse data scenarios. Agents must explore hostile environments with limited interaction budgets, without relying on environment-specific rewards or goals.
Innovation
The OneLife framework 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, forming a dynamic computation graph for inference and optimization.
Methodology
- �� OneLife framework 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.
- �� Forms a dynamic computation graph for inference and optimization.
Experiments
Experiments conducted in the Crafter-OO environment evaluate OneLife's ability to learn environment dynamics in unguided scenarios. State ranking and state fidelity evaluation protocols test the framework's performance across 23 scenarios.
Results
OneLife outperformed strong baselines in 16 out of 23 scenarios, demonstrating its ability to learn stochastic dynamics with sparse rule activation. Through simulated rollouts, OneLife successfully identified superior strategies in multi-step goal-oriented tasks.
Applications
The OneLife framework can be applied to autonomous exploration and planning in complex environments, especially in unguided scenarios. Its ability to learn with sparse data makes it promising for real-world applications.
Limitations & Outlook
OneLife may struggle in extremely sparse environments due to low rule activation frequency, hindering learning. In complex interaction scenarios, the combination of laws may increase computational complexity.
Plain Language Accessible to non-experts
Imagine exploring an unknown city, trying to understand its traffic rules. You have no map, no navigation, and can only infer through observation. OneLife acts like a smart assistant, gradually inferring the city's traffic laws by observing your actions and surroundings. It doesn't need external guidance; through observation and analysis, it helps you find the best routes and avoid traffic jams. Even if the city's traffic rules are complex and random, OneLife can learn and adjust to help you navigate better.
ELI14 Explained like you're 14
Imagine playing a super cool game where you only have one chance to explore a challenging world. No hints, no rewards, you have to figure out the rules yourself. OneLife is like your super assistant, gradually inferring the game's rules by observing your actions and surroundings. It doesn't need external guidance; through observation and analysis, it helps you find the best strategies and avoid danger. Even if the game's rules are complex and random, OneLife can learn and adjust to help you master the game.
Glossary
Symbolic World Model
Infers and represents environmental transition dynamics as executable programs.
Used to represent dynamic changes in complex environments.
Probabilistic Programming
A method for reasoning and optimization through probabilistic models.
Used for modeling world dynamics.
Precondition-Effect Structure
Structure of laws, defining applicability and effects.
Used to activate relevant laws.
Dynamic Computation Graph
Computational structure for inference and optimization based on law activation.
Used to avoid scalability challenges.
State Ranking
Ability to distinguish plausible future states from implausible ones.
Used to evaluate framework performance.
Open Questions Unanswered questions from this research
- 1 How to increase rule activation frequency in extremely sparse environments to enhance learning remains unexplored.
- 2 How to optimize law combinations to reduce computational complexity in complex interaction scenarios remains to be studied.
Applications
Immediate Applications
Autonomous Driving
OneLife framework can be used in autonomous driving systems for learning environment dynamics, aiding vehicles in navigating complex traffic environments.
Long-term Vision
Smart City Planning
By learning city traffic rules, OneLife framework can be used for smart city planning, optimizing traffic flow and resource allocation.
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.