327 citations · 970 across the 56 of their papers we have counts for
15 papers · 1 filter
Cost-Driven Representation Learning for Linear Quadratic Gaussian Control: Part I
Yi Tian, Kaiqing Zhang, Russ Tedrake +1
We study the task of learning state representations from potentially high-dimensional observations, with the goal of controlling an unknown partially observable system. We pursue a…
Offline Reinforcement Learning via Linear-Programming with Error-Bound Induced Constraints
Asuman Ozdaglar, Sarath Pattathil, Jiawei Zhang +1
Offline reinforcement learning (RL) aims to find an optimal policy for Markov decision processes (MDPs) using a pre-collected dataset. In this work, we revisit the linear programmi…
An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods
Yanli Liu, Kaiqing Zhang, Tamer Başar +1
In this paper, we revisit and improve the convergence of policy gradient (PG), natural PG (NPG) methods, and their variance-reduced variants, under general smooth policy parametriz…
Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies
Bin Hu, Kaiqing Zhang, Na Li +3
Gradient-based methods have been widely used for system design and optimization in diverse application domains. Recently, there has been a renewed interest in studying theoretical…
Symmetric (Optimistic) Natural Policy Gradient for Multi-agent Learning with Parameter Convergence
Sarath Pattathil, Kaiqing Zhang, Asuman Ozdaglar
Multi-agent interactions are increasingly important in the context of reinforcement learning, and the theoretical foundations of policy gradient methods have attracted surging rese…
Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?
Lirui Wang, Kaiqing Zhang, Yunzhu Li +2
Decentralized learning has been advocated and widely deployed to make efficient use of distributed datasets, with an extensive focus on supervised learning (SL) problems. Unfortuna…