@inproceedings{
ishfaq2024offline,
title={Offline Multitask Representation Learning for Reinforcement Learning},
author={Haque Ishfaq and Thanh Nguyen-Tang and Songtao Feng and Raman Arora and Mengdi Wang and Ming Yin and Doina Precup},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=72tRD2Mfjd}
}
We study offline multitask representation learning in reinforcement learning (RL), where a learner is provided with an offline dataset from different tasks that share a common representation and is asked to learn the shared representation. We theoretically investigate offline multitask low-rank RL, and propose a new algorithm called MORL for offline multitask representation learning. Furthermore, we examine downstream RL in reward-free, offline and online scenarios, where a new task is introduced to the agent that shares the same representation as the upstream offline tasks. Our theoretical results demonstrate the benefits of using the learned representation from the upstream offline task instead of directly learning the representation of the low-rank model.
Offline Multitask Representation Learning for Reinforcement Learning
Haque Ishfaq Thanh Nguyen-Tang Songtao Feng Raman Arora Mengdi Wang Ming Yin Doina Precup