REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments

Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman, Insup Lee

Keywords: Generalist Agents, In-Context Learning, Imitation Learning, Reinforcement Learning

TL;DR: We propose a retrieval-augmented generalist agent that can adapt to new environments via in-context learning without large models and vast datasets

Abstract: Do generalist agents only require large models pre-trained on massive amounts of data to rapidly adapt to new environments? We propose a novel approach to pre-train relatively small models and adapt them to unseen environments via in-context learning, without any finetuning. Our key idea is that retrieval offers a powerful bias for fast adaptation. Indeed, we demonstrate that even a simple retrieval-based 1-nearest neighbor agent offers a surprisingly strong baseline for today's state-of-the-art generalist agents. From this starting point, we construct a semi-parametric agent, REGENT, that trains a transformer-based policy on sequences of queries and retrieved neighbors. REGENT can generalize to unseen robotics and game-playing environments via retrieval augmentation and in-context learning, achieving this with up to 3x fewer parameters and up to an order-of-magnitude fewer pre-training datapoints, significantly outperforming today's state-of-the-art generalist agents.

Submission Number: 99