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.
The Psychedelic Era (1967-1969)
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Starting with their self-titled debut, the Stones were disciples of American blues and R&B. In high-resolution FLAC, the mono mixes of albums like The Rolling Stones and 12 X 5 gain a punchy, immediate quality that stereo remasters often lose. This era culminated in the psychedelic experimentation of Their Satanic Majesties Request. 2. The Golden Era (1968–1972) This is the "Big Four" run that defined rock history: Beggars Banquet (1968): The return to roots-rock. Let It Bleed (1969): A dark, apocalyptic masterpiece.
Beggars Banquet (1968): 50th Anniversary Remaster available in 24-bit/192kHz.
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The Psychedelic Era (1967-1969)
formats, primarily sourced from the 2002 ABKCO remasters and subsequent studio box sets. High-Resolution FLAC Sources
Post:
Starting with their self-titled debut, the Stones were disciples of American blues and R&B. In high-resolution FLAC, the mono mixes of albums like The Rolling Stones and 12 X 5 gain a punchy, immediate quality that stereo remasters often lose. This era culminated in the psychedelic experimentation of Their Satanic Majesties Request. 2. The Golden Era (1968–1972) This is the "Big Four" run that defined rock history: Beggars Banquet (1968): The return to roots-rock. Let It Bleed (1969): A dark, apocalyptic masterpiece.
Beggars Banquet (1968): 50th Anniversary Remaster available in 24-bit/192kHz.
To ensure you are getting legitimate, high-quality files rather than upsampled MP3s, stick to these official retailers:
Password: stonesflac (if applicable)
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.
"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
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@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}
}