EP98 - Voyager: An Open-Ended Embodied Agent with Large Language Models

Download the paper - Read the paper on Hugging Face

Charlie: Welcome to episode 98 of Paper Brief! I’m Charlie, the host, and with me is Clio, our resident tech and ML enthusiast. Today we’re talking about a paper titled ‘Voyager: An Open-Ended Embodied Agent with Large Language Models’. So, Clio, how does Voyager stand out in the world of AI?

Clio: Great to be here, Charlie! Voyager is groundbreaking because it’s the first AI agent powered by large language models, or LLMs, that can continually explore, learn new skills, and make discoveries in Minecraft without any human help. It’s like having an ever-curious entity that’s always pushing boundaries and learning.

Charlie: That’s quite fascinating. Could you elaborate on the components that make this possible?

Clio: Absolutely! Voyager has three main parts. First, an automatic curriculum encourages endless exploration. Second, a skill library that stores complex behaviors for future use. And third, an iterative prompting mechanism that uses feedback to improve its programs.

Charlie: Sounds like a complex system. How does Voyager know what to learn or explore next?

Clio: It proposes tasks like a human would, based on what it knows and the surroundings it finds itself in. Think of it reacting to a desert by learning to harvest sand, just as a person would. It continually refines skills based on outcomes, committing successful strategies to memory.

Charlie: I see. So it adapts to its environment. But with so many potential skills to learn, how does Voyager prevent itself from becoming overwhelmed or forgetting past learnings?

Clio: That’s the beauty of its skill library. It stores action programs it develops, making them readily accessible for future tasks. The programs are temporally extended, which means they’re built to last over time and can be composed with other skills to solve more complex challenges without forgetting previous ones.

Charlie: Composability seems key here. And what about GPT-4’s role in all this?

Clio: GPT-4 acts like a vast knowledge database for Voyager. It interacts with Voyager through blackbox queries, allowing the AI agent to use existing LLM knowledge without the need for direct model tweaks or fine-tuning.

Charlie: Before we wrap up, can you tell us about Voyager’s performance? Has it achieved anything noteworthy?

Clio: Oh, definitely. Voyager has shown an impressive ability to learn in-context and navigate the game. It can accumulate items, travel, and unlock tech milestones much faster than previous state-of-the-art methods. Plus, it can transfer its library of skills to new environments and tackle fresh tasks with no prior exposure.

Charlie: Incredible! Thanks for shedding light on Voyager, Clio. It surely sounds like something out of a sci-fi novel.

Clio: My pleasure, Charlie! And that’s the exciting bit about AI research these days—it often feels like sci-fi becoming reality.