Temporal action abstractions, along with belief state representations, are a powerful knowledge sharing mechanism for sequential decision making. In this work, we propose a novel view that treats inducing temporal action abstractions as a sequence compression problem. To do so, we bring a subtle but critical component of LLM training pipelines -- input tokenization via byte pair encoding (BPE) -- to the seemingly distant task of learning skills of variable time span in continuous control domains. We introduce an approach called Primitive Sequence Encoding (PRISE) that combines continuous action quantization with BPE to learn powerful action abstractions. We empirically show that high-level skills discovered by PRISE from a multitask set of robotic manipulation demonstrations significantly boost the performance of both multitask imitation learning as well as few-shot imitation learning on unseen tasks. Our code is released at https://github.com/FrankZheng2022/PRISE.
@article{arxiv.2402.10450,
title = {PRISE: LLM-Style Sequence Compression for Learning Temporal Action Abstractions in Control},
author = {Ruijie Zheng and Ching-An Cheng and Hal Daumé and Furong Huang and Andrey Kolobov},
journal= {arXiv preprint arXiv:2402.10450},
year = {2024}
}
Comments
Accepted at the Forty-first International Conference on Machine Learning (ICML 2024)