Because there is no LLM-based agents that work out of the box for Minecraft, we make our best
effort to select a number of representative algorithms as baselines. These methods are originally
designed only for NLP tasks without embodiment, therefore we have to re-interpret them to be
executable in MineDojo and compatible with our experimental setting:
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.
Reflexion [30] is built on top of ReAct [29] with self-reflection to infer more intuitive future actions.
We provide it with execution errors and our self-verification module.
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
VOYAGER- An Open-Ended Embodied Agent with Large Language Models