Paper - Voyager: An Open-Ended Embodied Agent with Large Language Models
FreeAn open-ended embodied agent that learns and explores Minecraft autonomously using LLMs.
About Paper - Voyager: An Open-Ended Embodied Agent with Large Language Models
Voyager is the first LLM-powered embodied lifelong learning agent designed for the Minecraft environment. It uses OpenAI's GPT-4 to continuously explore the world, acquire diverse skills, and make novel discoveries without human intervention. The system features three key components: an automatic curriculum that maximizes exploration, an ever-growing skill library of executable code for storing and retrieving complex behaviors, and an iterative prompting mechanism that incorporates environment feedback, execution errors, and self-verification for program improvement. Empirically, Voyager obtains 3.3x more unique items, travels 2.3x longer distances, and unlocks key tech tree milestones up to 15.3x faster than prior state-of-the-art methods. The full codebase and prompts are open-source.
Key Features
Pros & Cons
- Achieves 3.3x more unique items, 2.3x longer travel distances, and 15.3x faster tech tree progression than prior SOTA
- Learns without human intervention or fine-tuning of model parameters
- Skills are interpretable, reusable, and composable
- Demonstrates strong in-context lifelong learning capability
- Open-source codebase and prompts available for reproducibility
- Relies on GPT-4 API which is expensive and proprietary (blackbox queries)
- Currently restricted to the Minecraft environment only
- Performance depends on the underlying LLM's capabilities and API availability
- May require significant computational resources for running experiments