Uni[MASK]: Unified Inference in Sequential Decision Problems - Carroll, Micah; Paradise, Orr; Lin, Jessy; Stevenson, Raluca; Sun, Mingfei; Bignell, Dave; Milani, Stephanie; Hofmann, Katja; Hausknecht, Matthew; Dragan, Anca; Devlin, Sam (2022 Neural Information Processing Systems, 2022)
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same idea also applies naturally to sequential decision-making, where many well-studied tasks like behavior cloning, offline reinforcement learning, inverse dynamics, and waypoint conditioning correspond to different sequence maskings over a sequence of states, actions, and returns. We introduce the UniMASK framework, which provides a unified way to specify models which can be trained on many different sequential decision-making tasks. We show that a single UniMASK model is often capable of carrying out many tasks with performance similar to or better than single-task models. Additionally, after fine-tuning, our UniMASK models consistently outperform comparable single-task models.