Abstract

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.

Voyager discovers new Minecraft items and skills continually by self-driven exploration, significantly outperforming the baselines.

Introduction

Building generally capable embodied agents that continuously explore, plan, and develop new skills in open-ended worlds is a grand challenge for the AI community. Classical approaches employ reinforcement learning (RL) and imitation learning that operate on primitive actions, which could be challenging for systematic exploration, interpretability, and generalization. Recent advances in large language model (LLM) based agents harness the world knowledge encapsulated in pre-trained LLMs to generate consistent action plans or executable policies. They are applied to embodied tasks like games and robotics, as well as NLP tasks without embodiment. However, these agents are not lifelong learners that can progressively acquire, update, accumulate, and transfer knowledge over extended time spans.

Let us consider Minecraft as an example. Unlike most other games studied in AI, Minecraft does not impose a predefined end goal or a fixed storyline but rather provides a unique playground with endless possibilities. An effective lifelong learning agent should have similar capabilities as human players: (1) propose suitable tasks based on its current skill level and world state, e.g., learn to harvest sand and cactus before iron if it finds itself in a desert rather than a forest; (2) refine skills based on environment feedback and commit mastered skills to memory for future reuse in similar situations (e.g. fighting zombies is similar to fighting spiders); (3) continually explore the world and seek out new tasks in a self-driven manner.

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as Quinn, a tech-savvy ally from the 2003 timeline. Michael Shanks in a key supporting parental role.

Lucy must navigate the unfamiliar world of the early 2000s, blending in with the teens of that era while desperately trying to alter the past and save her sister. The movie is a race against time, filled with nostalgic references (including music from Avril Lavigne and Vanessa Carlton), teen drama, and slasher-horror sequences. As Lucy gets closer to the truth, she must also grapple with the classic time-travel paradox: can she change the past without erasing her own future—and the entire timeline along with it?

When searching for specific files like "movievillas timecut2024720pwebdlhine," users should be aware of the security implications.

: Streaming or downloading pirated content violates copyright laws and can lead to warnings from your ISP or legal consequences.

The story follows a teenager in 2024 who accidentally travels back to 2003, just days before a masked killer murders her older sister. She must navigate the complexities of time travel to save her sister while avoiding the destruction of the future. Genre: A blend of Horror, Suspense, Thriller, and Sci-Fi.

Short for "Web Download," this specifies the file source rip type, meaning the file was losslessly captured directly from an official streaming service.

The search for "movievillas timecut2024720pwebdlhine" highlights the demand for timely access to new genre films like Time Cut (2024), particularly in accessible formats (720p WebDL) and for specific regional audiences (Hindi). As audiences continue to seek out diverse content, the digital release landscape continues to adapt, offering various ways to consume films.

An investigation into the search term reveals that it is not a traditional editorial keyword, but rather a highly specific, fragmented string used to find and illegally stream or download the 2024 sci-fi slasher film Time Cut .

The safest way to watch movies is through official, licensed streaming services (e.g., Netflix, Prime Video, Disney+), which ensures high security and quality.

as Lucy Field, the central time-traveling protagonist. Antonia Gentry as Summer Field, Lucy's doomed older sister.

The search term is a specific combination of words that points directly to the world of online movie downloading. Unpacking this keyword gives us a fascinating look at modern movie-watching habits: it combines a film title, a source website, a video quality standard, and a language preference. This article breaks down exactly what each part means, provides an expert analysis of the movie Time Cut , explores the technical details of the "720p WEB-DL" format, and, most importantly, issues a serious warning about the safety risks of using sites like Movievillas.

Released on Netflix in October 2024, Time Cut is a sci-fi slasher thriller directed by Hannah MacPherson. The film combines the nostalgia of the early 2000s with modern horror tropes.

as Quinn, a tech-savvy ally from the 2003 timeline. Michael Shanks in a key supporting parental role.

Conclusion

In this work, we introduce Voyager, the first LLM-powered embodied lifelong learning agent, which leverages GPT-4 to explore the world continuously, develop increasingly sophisticated skills, and make new discoveries consistently without human intervention. Voyager exhibits superior performance in discovering novel items, unlocking the Minecraft tech tree, traversing diverse terrains, and applying its learned skill library to unseen tasks in a newly instantiated world. Voyager serves as a starting point to develop powerful generalist agents without tuning the model parameters.

Media Coverage

"They Plugged GPT-4 Into Minecraft—and Unearthed New Potential for AI. The bot plays the video game by tapping the text generator to pick up new skills, suggesting that the tech behind ChatGPT could automate many workplace tasks." - Will Knight, WIRED

"The Voyager project shows, however, that by pairing GPT-4’s abilities with agent software that stores sequences that work and remembers what does not, developers can achieve stunning results." - John Koetsier, Forbes

"Voyager, the GTP-4 bot that plays Minecraft autonomously and better than anyone else" - Ruetir

"This AI used GPT-4 to become an expert Minecraft player" - Devin Coldewey, TechCrunch

Coverage Index: [Atmarkit] [Career Engine] [Crast.net] [Daily Top Feeds] [Entrepreneur en Espanol] [Finance Jxyuging] [Forbes] [Forbes Argentina] [Gaming Deputy] [Gearrice] [Haberik] [Head Topics] [InfoQ] [ITmedia News] [Mark Tech Post] [Medium] [MSN] [Note] [Noticias de Hoy] [Ruetir] [Stock HK] [Tech Tribune France] [TechCrunch] [TechBeezer] [Toutiao] [US Times Post] [VN Explorer] [WIRED] [Zaker]

Team

Guanzhi Wang

Yuqi Xie

Yunfan Jiang^*

Ajay Mandlekar^*

Chaowei Xiao

Yuke Zhu

Linxi "Jim" Fan^†

Anima Anandkumar^†

* Equal Contribution † Equal Advising

BibTeX

@article{wang2023voyager,
  title   = {Voyager: An Open-Ended Embodied Agent with Large Language Models},
  author  = {Guanzhi Wang and Yuqi Xie and Yunfan Jiang and Ajay Mandlekar and Chaowei Xiao and Yuke Zhu and Linxi Fan and Anima Anandkumar},
  year    = {2023},
  journal = {arXiv preprint arXiv: Arxiv-2305.16291}
}