82 citations · 546 across the 65 of their papers we have counts for
65 papers
First-order Policy Optimization for Robust Policy Evaluation
Yan Li, Guanghui Lan
We adopt a policy optimization viewpoint towards policy evaluation for robust Markov decision process with -rectangular ambiguity sets. The developed method, named firs…
Accelerated stochastic approximation with state-dependent noise
Sasila Ilandarideva, Anatoli Juditsky, Guanghui Lan +1
We consider a class of stochastic smooth convex optimization problems under rather general assumptions on the noise in the stochastic gradient observation. As opposed to the classi…
Numerical Methods for Convex Multistage Stochastic Optimization
Guanghui Lan, Alexander Shapiro
Optimization problems involving sequential decisions in a stochastic environment were studied in Stochastic Programming (SP), Stochastic Optimal Control (SOC) and Markov Decision P…
Policy Mirror Descent Inherently Explores Action Space
Yan Li, Guanghui Lan
Explicit exploration in the action space was assumed to be indispensable for online policy gradient methods to avoid a drastic degradation in sample complexity, for solving general…
Dual dynamic programming for stochastic programs over an infinite horizon
Caleb Ju, Guanghui Lan
We consider solving stochastic programs over an infinite horizon. By leveraging the stationarity of the problem, we develop a novel continually-exploring infinite-horizon explorati…
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…