with a blackbox LLM (GPT-4 [35]) through prompting and in-context learning [36–38]. Our approach
bypasses the need for model parameter access and explicit gradient-based training or finetuning.
More specifically, VOYAGER attempts to solve progressively harder tasks proposed by the automatic
curriculum, which takes into account the exploration progress and the agent’s state. The curriculum
is generated by GPT-4 based on the overarching goal of “discovering as many diverse things as
possible”. This approach can be perceived as an in-context form of novelty search [39, 40]. VOYAGER
VOYAGER- An Open-Ended Embodied Agent with Large Language Models