into multiple subgoals and executing them in a ReAct-style loop. We re-implement AutoGPT by
using GPT-4 to do task decomposition and provide it with the agent states, environment feedback,
and execution errors as observations for subgoal execution. Compared with VOYAGER, AutoGPT
lacks the skill library for accumulating knowledge, self-verification for assessing task success, and
automatic curriculum for open-ended exploration. During each subgoal execution, if no execution
error occurs, we consider the subgoal completed and proceed to the next one. Otherwise, we refine
the program until three rounds of code refinement (equivalent to four rounds of code generation)
are completed, and then move on to the next subgoal. If three consecutive subgoals do not result in
acquiring a new item, we replan by rerunning the task decomposition.
The task is “explore the world and get as many items as possible” for all baselines.
VOYAGER- An Open-Ended Embodied Agent with Large Language Models