82 citations · 546 across the 65 of their papers we have counts for
10 papers · 1 filter
Policy Optimization over General State and Action Spaces
Caleb Ju, Guanghui Lan
Reinforcement learning (RL) problems over general state and action spaces are notoriously challenging. In contrast to the tableau setting, one can not enumerate all the states and…
Solving Convex Smooth Function Constrained Optimization Is Almost As Easy As Unconstrained Optimization
Zhe Zhang, Guanghui Lan
While Nesterov's Accelerated Gradient Descent (AGD) efficiently solves constrained problems when the constraint set $X \subseteq \bbr^n$ is simple and easy to project onto, it rema…
Projection-Free Functional Constrained Optimization for Risk Aversion and Sparsity Control
Yi Cheng, Guanghui Lan, Saeed Masiha +1
We study projection-free methods for functional constrained optimization with convex or smooth nonconvex objectives. Such problems arise in applications such as portfolio optimizat…
First-order Policy Optimization for Robust Markov Decision Process
Yan Li, Guanghui Lan, Tuo Zhao
We consider the problem of solving robust Markov decision process (MDP), which involves a set of discounted, finite state, finite action space MDPs with uncertain transition kernel…
Level Constrained First Order Methods for Function Constrained Optimization
Digvijay Boob, Qi Deng, Guanghui Lan
We present a new feasible proximal gradient method for constrained optimization where both the objective and constraint functions are given by the summation of a smooth, possibly n…
Stochastic first-order methods for average-reward Markov decision processes
Tianjiao Li, Feiyang Wu, Guanghui Lan
We study average-reward Markov decision processes (AMDPs) and develop novel first-order methods with strong theoretical guarantees for both policy optimization and policy evaluatio…