title: Voyager: An Open-Ended Embodied Agent with Large Language Models
summary: 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/.
paper_authors._id: 64700d67644111130400caaf
paper_authors.name: Guanzhi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-26T18:35:19.784Z
paper_authors._id: 628353d76db5127097ce65cd
paper_authors.avatarUrl: /avatars/b8e519114d0926db4e2f9a0514e5cb32.svg
paper_authors.fullname: Guanzhi Wang
paper_authors.type: user
paper_authors.user: guanzhi
paper_authors._id: 64700d67644111130400cab0
paper_authors.name: Yuqi Xie
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-26T13:57:22.122Z
paper_authors._id: 63f3d5f04745321de355fb26
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1676924371675-noauth.png
paper_authors.fullname: Yuqi Xie
paper_authors.type: user
paper_authors.user: xieleo5
paper_authors._id: 64700d67644111130400cab1
paper_authors.name: Yunfan Jiang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-26T17:15:03.477Z
paper_authors._id: 61e9f5398c237a147a3f4ab5
paper_authors.avatarUrl: /avatars/afd4ec17cb132b5ab56e50a678c4786d.svg
paper_authors.fullname: Yunfan Jiang
paper_authors.type: user
paper_authors.user: yunfanj
paper_authors._id: 64700d67644111130400cab2
paper_authors.name: Ajay Mandlekar
paper_authors._id: 64700d67644111130400cab3
paper_authors.name: Chaowei Xiao
paper_authors._id: 64700d67644111130400cab4
paper_authors.name: Yuke Zhu
paper_authors._id: 64700d67644111130400cab5
paper_authors.name: Linxi Fan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-28T07:08:29.114Z
paper_authors._id: 628352f309aa80237c6c3e4b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/628352f309aa80237c6c3e4b/W2dkeN-jJ137HfbYKo088.jpeg
paper_authors.fullname: Jim Fan
paper_authors.type: user
paper_authors.user: jimfan
paper_authors._id: 64700d67644111130400cab6
paper_authors.name: Anima Anandkumar
paper_title: Voyager: An Open-Ended Embodied Agent with Large Language Models
paper_summary: 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/.
paper_ai_summary: Voyager is an LLM-powered agent in Minecraft that autonomously explores, learns, and discovers skills through a curriculum, skill library, and prompting mechanism, demonstrating superior lifelong learning and task-solving capabilities.
paper_ai_keywords: LLM-powered embodied lifelong learning agent automatic curriculum skill library iterative prompting mechanism environment feedback execution errors self-verification blackbox queries GPT-4 temporal extension interpretable compositional catastrophic forgetting in-context lifelong learning unique items distances tech tree milestones novel tasks generalization Minedojo