Voyager: An Open-Ended Embodied Agent with Large Language Models
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 3.3× more unique items, travels 2.3× longer distances, and unlocks key tech tree milestones up to 15.3× faster than prior SOTA.
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 [16, 22, 19]. They are applied to embodied tasks like games and robotics [23–27], as well as NLP tasks without embodiment [28–30]. However, these agents are not lifelong learners that can progressively acquire, update, accumulate, and transfer knowledge over extended time spans [31, 32].
We argue that 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 environmental 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.
More specifically, VOYAGER attempts to solve progressively harder tasks proposed by the automatic curriculum, which takes into account the exploration progress and the agent’s state. The curriculum is generated by GPT-4 based on the overarching goal of “discovering as many diverse things as possible”. This approach can be perceived as an in-context form of novelty search [39, 40]. VOYAGER incrementally builds a skill library by storing the action programs that help solve a task successfully. Each program is indexed by the embedding of its description, which can be retrieved in similar situations in the future. Complex skills can be synthesized by composing simpler programs, which compounds VOYAGER’s capabilities rapidly over time and alleviates catastrophic forgetting in other continual learning methods [31, 32].
Empirically, VOYAGER demonstrates strong in-context lifelong learning capabilities. It can construct an ever-growing skill library of action programs that are reusable, interpretable, and generalizable to novel tasks.