no code implementations • 18 Mar 2024 • Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup
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.
no code implementations • 17 Aug 2023 • Songtao Feng, Ming Yin, Yu-Xiang Wang, Jing Yang, Yingbin Liang
In this work, we propose a model-free stage-based Q-learning algorithm and show that it achieves the same sample complexity as the best model-based algorithm, and hence for the first time demonstrate that model-free algorithms can enjoy the same optimality in the $H$ dependence as model-based algorithms.
no code implementations • 1 Jun 2023 • Songtao Feng, Ming Yin, Ruiquan Huang, Yu-Xiang Wang, Jing Yang, Yingbin Liang
To the best of our knowledge, this is the first dynamic regret analysis in non-stationary MDPs with general function approximation.
no code implementations • 13 Jun 2022 • Yuan Cheng, Songtao Feng, Jing Yang, Hong Zhang, Yingbin Liang
To the best of our knowledge, this is the first theoretical study that characterizes the benefit of representation learning in exploration-based reward-free multitask RL for both upstream and downstream tasks.