61 citations · 750 across the 66 of their papers we have counts for
9 papers · 1 filter
Global Convergence of Two-timescale Actor-Critic for Solving Linear Quadratic Regulator
Xuyang Chen, Jingliang Duan, Yingbin Liang +1
The actor-critic (AC) reinforcement learning algorithms have been the powerhouse behind many challenging applications. Nevertheless, its convergence is fragile in general. To study…
Provable Generalization of Overparameterized Meta-learning Trained with SGD
Yu Huang, Yingbin Liang, Longbo Huang
Despite the superior empirical success of deep meta-learning, theoretical understanding of overparameterized meta-learning is still limited. This paper studies the generalization o…
Provable Benefit of Multitask Representation Learning in Reinforcement Learning
Yuan Cheng, Songtao Feng, Jing Yang +2
As representation learning becomes a powerful technique to reduce sample complexity in reinforcement learning (RL) in practice, theoretical understanding of its advantage is still…
Safe Exploration Incurs Nearly No Additional Sample Complexity for Reward-free RL
Ruiquan Huang, Jing Yang, Yingbin Liang
Reward-free reinforcement learning (RF-RL), a recently introduced RL paradigm, relies on random action-taking to explore the unknown environment without any reward feedback informa…
Provably Efficient Offline Reinforcement Learning with Trajectory-Wise Reward
Tengyu Xu, Yue Wang, Shaofeng Zou +1
The remarkable success of reinforcement learning (RL) heavily relies on observing the reward of every visited state-action pair. In many real world applications, however, an agent…
Will Bilevel Optimizers Benefit from Loops
Kaiyi Ji, Mingrui Liu, Yingbin Liang +1
Bilevel optimization has arisen as a powerful tool for solving a variety of machine learning problems. Two current popular bilevel optimizers AID-BiO and ITD-BiO naturally involve…