Work

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

Year: 2024

Type: conference-paper

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.

Source: neural information processing systems

DOI: 10.52202/079017-2243

Authors: Hao Tang, Darren Key, Kevin Ellis

Language: English

Topic: AI-based Problem Solving and Planning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Cisco Systems

Award: SHF: Small: Synthesizing Mixed Discrete/Continuous Programs with the Neurosymbolic Librarian

OpenAlex ID: W4415796843

FWCI: 2.599

Cites:

Cited by: 9

Related to: 10