42 citations · 102 across the 4 of their papers we have counts for
4 papers
Scalable Bilinear Learning Using State and Action Features
Yichen Chen, Lihong Li, Mengdi Wang
Approximate linear programming (ALP) represents one of the major algorithmic families to solve large-scale Markov decision processes (MDP). In this work, we study a primal-dual for…
Lower Bound On the Computational Complexity of Discounted Markov Decision Problems
Yichen Chen, Mengdi Wang
We study the computational complexity of the infinite-horizon discounted-reward Markov Decision Problem (MDP) with a finite state space and a finite action space $|…
Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning
Yichen Chen, Mengdi Wang
We study the online estimation of the optimal policy of a Markov decision process (MDP). We propose a class of Stochastic Primal-Dual (SPD) methods which exploit the inherent minim…
Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints
Mengdi Wang, Yichen Chen, Jialin Liu +1
Consider convex optimization problems subject to a large number of constraints. We focus on stochastic problems in which the objective takes the form of expected values and the fea…