UniMASK: Unified Inference in Sequential Decision Problems

• URL: http://arxiv.org/abs/2211.10869v1
• Date: Sun, 20 Nov 2022 04:54:49 GMT
• Title: UniMASK: Unified Inference in Sequential Decision Problems
• Authors: Micah Carroll, Orr Paradise, Jessy Lin, Raluca Georgescu, Mingfei Sun, David Bignell, Stephanie Milani, Katja Hofmann, Matthew Hausknecht, Anca Dragan, Sam Devlin
• Abstract summary: We introduce the UniMASK framework, which provides a unified way to specify models which can be trained on many different sequential decision-making tasks. A single UniMASK model is often capable of carrying out many tasks with performance similar to or better than single-task models.
• Score: 17.09745648221254
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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. Our code is publicly available at https://github.com/micahcarroll/uniMASK.

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