Voyager: An Open-Ended Embodied Agent with Large Language Models
Authors: name: Guanzhi Wang, name: Yuqi Xie, name: Yunfan Jiang, name: Ajay Mandlekar, name: Chaowei Xiao, name: Yuke Zhu, name: Linxi Fan, name: Anima Anandkumar

We introduce Voyager, the first LLM-powered embodied lifelong learning agent
in Minecraft that continuously explores the world, acquires diverse skills, and
makes novel discoveries without human intervention. Voyager consists of three
key components: 1) an automatic curriculum that maximizes exploration, 2) an
ever-growing skill library of executable code for storing and retrieving
complex behaviors, and 3) a new iterative prompting mechanism that incorporates
environment feedback, execution errors, and self-verification for program
improvement. Voyager interacts with GPT-4 via blackbox queries, which bypasses
the need for model parameter fine-tuning. The skills developed by Voyager are
temporally extended, interpretable, and compositional, which compounds the
agent's abilities rapidly and alleviates catastrophic forgetting. Empirically,
Voyager shows strong in-context lifelong learning capability and exhibits
exceptional proficiency in playing Minecraft. It obtains 3.3x more unique
items, travels 2.3x longer distances, and unlocks key tech tree milestones up
to 15.3x faster than prior SOTA. Voyager is able to utilize the learned skill
library in a new Minecraft world to solve novel tasks from scratch, while other
techniques struggle to generalize. We open-source our full codebase and prompts
at https://voyager.minedojo.org/.