Provably efficient multi-task reinforcement learning with model transfer

Chicheng Zhang; Zhi Wang

NeurIPS 2021poster21 citations

Provably efficient multi-task reinforcement learning with model transfer

Abstract

We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes (MDPs). We formulate a heterogeneous multi-player RL problem, in which a group of players concurrently face similar but not necessarily identical MDPs, with a goal of improving their collective performance through inter-player information sharing. We design and analyze a model-based algorithm, and provide gap-dependent and gap-independent regret upper and lower bounds that characterize the intrinsic complexity of the problem.

Multi-task learningProvably efficient reinforcement learningModel transfer

BibTeX

@inproceedings{
zhang2021provably,
title={Provably efficient multi-task reinforcement learning with model transfer},
author={Chicheng Zhang and Zhi Wang},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=qPOeyokHXT8}
}