ReAct [29] uses chain-of-thought prompting [47] by generating both reasoning traces and action
plans with LLMs. We provide it with our environment feedback and the agent states as observations.
ReAct undergoes one round of code generation from scratch, followed by three rounds of code
refinement. This process is then repeated until the maximum prompting iteration is reached.
Reflexion [30] is built on top of ReAct [29] with self-reflection to infer more intuitive future actions.
We provide it with environment feedback, the agent states, execution errors, and our self-verification
module. Similar to ReAct, Reflexion undergoes one round of code generation from scratch, followed
by three rounds of code refinement. This process is then repeated until the maximum prompting
iteration is reached.
AutoGPT [28] is a popular software tool that automates NLP tasks by decomposing a high-level goal
into multiple subgoals and executing them in a ReAct-style loop. We re-implement AutoGPT by
VOYAGER- An Open-Ended Embodied Agent with Large Language Models