Voyager Minedojo is a groundbreaking embodied agent immersed in the Minecraft universe, setting a remarkable milestone as the first agent of its kind to achieve lifelong learning autonomously, without human intervention.
The core of Voyager is composed of three pivotal components: an automatic curriculum, a skill repository, and an iterative prompting mechanism. The automatic curriculum is the driving force behind extensive exploration, constantly assessing the agent’s progress and situational context to generate tasks that revolve around uncovering diverse and novel phenomena—a concept reminiscent of in-context novelty search.
Within the skill repository, complex behaviors are meticulously cataloged and readily accessible. Each skill is indexed using embeddings derived from its description, simplifying retrieval in analogous scenarios. This architecture allows for the synthesis of intricate skills through the composition of simpler programs, continually bolstering Voyager’s capabilities and safeguarding against catastrophic memory loss.
The iterative prompting mechanism elevates Voyager’s learning process by factoring in environmental feedback, execution errors, and self-verification to refine and improve its programming. It serves as a mechanism for Voyager to learn from its mistakes, gradually honing its skills.
Voyager engages with GPT-4, a formidable large language model, through blackbox queries, obviating the need for intricate model parameter fine-tuning. In rigorous experiments, Voyager showcases remarkable lifelong learning proficiency, particularly in the realm of Minecraft.
It surpasses prior state-of-the-art methods by amassing a greater variety of unique items, traversing more extensive distances, and accomplishing pivotal milestones on the technological progression tree at an accelerated pace. Moreover, Voyager excels in the domain of adapting to fresh challenges within novel Minecraft environments, consistently demonstrating its prowess.
In sum, Voyager Minedojo seamlessly marries the potential of large language models with the realm of embodied learning, resulting in an agent that thrives in open-ended landscapes like Minecraft. It embodies a continuous quest for exploration, skill refinement, and groundbreaking discoveries.