Uni[MASK]: Unified Inference in Sequential Decision Problems

Abstract

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 RL, 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.

Publication
Oral presentation at The 35th Annual Conference on Neural Information Processing Systems (NeurIPS 2022); The first workshop on Generalizable Policy Learning in the Physical World at the Tenth International Conference on Learning Representations (GPLPW @ ICLR 2022)