84 citations · 603 across the 59 of their papers we have counts for
13 papers · 1 filter
On the Effectiveness of Iterative Learning Control
Anirudh Vemula, Wen Sun, Maxim Likhachev +1
Iterative learning control (ILC) is a powerful technique for high performance tracking in the presence of modeling errors for optimal control applications. There is extensive prior…
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning
Haitong Ma, Changliu Liu, Shengbo Eben Li +3
In the trial-and-error mechanism of reinforcement learning (RL), a notorious contradiction arises when we expect to learn a safe policy: how to learn a safe policy without enough d…
Representation Learning for Online and Offline RL in Low-rank MDPs
Masatoshi Uehara, Xuezhou Zhang, Wen Sun
This work studies the question of Representation Learning in RL: how can we learn a compact low-dimensional representation such that on top of the representation we can perform RL…
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
Ye Yuan, Yuda Song, Zhengyi Luo +2
An agent's functionality is largely determined by its design, i.e., skeletal structure and joint attributes (e.g., length, size, strength). However, finding the optimal agent desig…
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
Yuda Song, Wen Sun
Model-based Reinforcement Learning (RL) is a popular learning paradigm due to its potential sample efficiency compared to model-free RL. However, existing empirical model-based RL…
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage
Masatoshi Uehara, Wen Sun
We study model-based offline Reinforcement Learning with general function approximation without a full coverage assumption on the offline data distribution. We present an algorithm…
Corruption-Robust Offline Reinforcement Learning
Xuezhou Zhang, Yiding Chen, Jerry Zhu +1
We study the adversarial robustness in offline reinforcement learning. Given a batch dataset consisting of tuples , an adversary is allowed to arbitrarily modify …
Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage
Jonathan D. Chang, Masatoshi Uehara, Dhruv Sreenivas +2
This paper studies offline Imitation Learning (IL) where an agent learns to imitate an expert demonstrator without additional online environment interactions. Instead, the learner…
Bilinear Classes: A Structural Framework for Provable Generalization in RL
Simon S. Du, Sham M. Kakade, Jason D. Lee +4
This work introduces Bilinear Classes, a new structural framework, which permit generalization in reinforcement learning in a wide variety of settings through the use of function a…
Fairness of Exposure in Stochastic Bandits
Lequn Wang, Yiwei Bai, Wen Sun +1
Contextual bandit algorithms have become widely used for recommendation in online systems (e.g. marketplaces, music streaming, news), where they now wield substantial influence on…
MobILE: Model-Based Imitation Learning From Observation Alone
Rahul Kidambi, Jonathan Chang, Wen Sun
This paper studies Imitation Learning from Observations alone (ILFO) where the learner is presented with expert demonstrations that consist only of states visited by an expert (wit…
Robust Policy Gradient against Strong Data Corruption
Xuezhou Zhang, Yiding Chen, Xiaojin Zhu +1
We study the problem of robust reinforcement learning under adversarial corruption on both rewards and transitions. Our attack model assumes an \textit{adaptive} adversary who can…
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency
Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang +3
We offer a theoretical characterization of off-policy evaluation (OPE) in reinforcement learning using function approximation for marginal importance weights and -functions when…