WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment (2402.12275v3)

Abstract: We give a model-based agent that builds a Python program representing its knowledge of the world based on its interactions with the environment. The world model tries to explain its interactions, while also being optimistic about what reward it can achieve. We define this optimism as a logical constraint between a program and a planner. We study our agent on gridworlds, and on task planning, finding our approach is more sample-efficient compared to deep RL, more compute-efficient compared to ReAct-style agents, and that it can transfer its knowledge across environments by editing its code.

Building World Models by Writing Code and Interacting with the Environment: A Professional Overview

The paper "WorldCoder, a Model-Based LLM Agent: Building World Models by Writing Code and Interacting with the Environment" proposes an innovative approach to developing LLM-based agents capable of acquiring and representing knowledge as Python code. This strategy addresses two core challenges in AI: learning world models from limited interactions and effectively communicating knowledge through symbolic representation.

Core Contributions

1. Symbolic World Models: The research emphasizes learning world models through symbolic representations, which are expressed as Python programs. Unlike traditional deep reinforcement learning (RL) approaches, which are typically neural and learned, the symbolic representation here is both learned and fixed, lending itself to greater transparency and inspection capabilities. The authors argue that Python, with its readability and extensive ecosystem, offers an excellent medium for these models.
2. Efficiency in Learning and Computation: A key highlight of this work is its focus on sample-efficient and compute-efficient learning. The framework is analyzed against deep RL and other LLM agents, revealing its superior sample efficiency—requiring fewer environment interactions to build a coherent model. It also stands out for compute efficiency, needing fewer LLM calls to solve tasks once the world model is established.
3. Optimism under Uncertainty: The paper introduces a world model learning objective that favors optimistic planning. The optimizer seeks models suggesting that achieving positive rewards is possible, guiding the agent to derive reward structures that stimulate goal-oriented exploration and reduce environment interactions required to learn a model.
4. Transferability and Instruction Adherence: Apart from merely sample efficiency, WorldCoder's symbolic models enable strong zero-shot performance on new tasks, demonstrating the system's ability to adapt to new goals and transfer learned strategies across tasks without retraining.

Methodology and Experimental Setup

The methodology is centered on a Contextual Markov Decision Process that enables the agent to model and reason about world transitions and context-specific rewards via Python code synthesis. The process extends beyond mere programming; it involves creating a representation of the transition function and a reward mechanism that respects goal-achievement criteria.

Experiments are conducted in grid-based environments like Sokoban and Minigrid to validate the claims of efficiency and transfer. In these domains, WorldCoder outperforms traditional deep RL approaches, which can take millions of interactions to achieve rudimentary competence. Notably, the implementation requires only several hundred code-tokens-related LLM interactions, contrasting with the exorbitant LLM interactions seen in approaches like ReAct.

Implications and Future Directions

The use of Python for world modeling presents clear advantages in transparency and ease of integration with existing software tools. The approach sets a precedent for leveraging symbolic representations, which can enhance interpretability—a key requirement in several AI applications.

For future work, integrating raw perceptual inputs with the symbolic models remains a challenge that needs addressing. This crucial step would involve bridging the gap between noisy, continuous real-world data and the discrete symbols used in computational models. Moreover, addressing probabilistic environments, which existing frameworks handle inadequately, could expand the applicability of this paradigm.

This research contributes significantly to the AI community, suggesting that investing in adaptable, interpretable world models can lead to more robust and versatile AI agents.