35 citations · 182 across the 29 of their papers we have counts for
29 papers
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning
Dongsheng Ding, Xiaohan Wei, Zhuoran Yang +2
We examine online safe multi-agent reinforcement learning using constrained Markov games in which agents compete by maximizing their expected total rewards under a constraint on ex…
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions
Shuang Qiu, Xiaohan Wei, Jieping Ye +2
While single-agent policy optimization in a fixed environment has attracted a lot of research attention recently in the reinforcement learning community, much less is known theoret…
Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning
Shuang Qiu, Zhuoran Yang, Xiaohan Wei +2
Temporal-Difference (TD) learning with nonlinear smooth function approximation for policy evaluation has achieved great success in modern reinforcement learning. It is shown that s…
Gradient-Variation Bound for Online Convex Optimization with Constraints
Shuang Qiu, Xiaohan Wei, Mladen Kolar
We study online convex optimization with constraints consisting of multiple functional constraints and a relatively simple constraint set, such as a Euclidean ball. As enforcing th…
II. High Dimensional Estimation under Weak Moment Assumptions: Structured Recovery and Matrix Estimation
Xiaohan Wei
The purpose of this thesis is to develop new theories on high-dimensional structured signal recovery under a rather weak assumption on the measurements that only a finite number of…
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss
Shuang Qiu, Xiaohan Wei, Zhuoran Yang +2
We consider online learning for episodic stochastically constrained Markov decision processes (CMDPs), which plays a central role in ensuring the safety of reinforcement learning.…