7 citations · 7 across the 7 of their papers we have counts for
7 papers
Sample Average Approximation for Distributionally Robust Optimization with -divergences
Yan Li
It is well known that estimating the expectation of any given bounded random variable with values in has two basic properties: (1) the number of samples needed scales at…
Computational Hardness of Static Distributionally Robust Markov Decision Processes
Yan Li
We present some hardness results on finding the optimal policy for the static formulation of distributionally robust Markov decision processes. We construct problem instances such…
Risk-averse formulations of Stochastic Optimal Control and Markov Decision Processes
Alexander Shapiro, Yan Li
The aim of this paper is to investigate risk-averse and distributionally robust modeling of Stochastic Optimal Control (SOC) and Markov Decision Process (MDP). We discuss construct…
Distributionally robust stochastic optimal control
Alexander Shapiro, Yan Li
The main goal of this paper is to discuss the construction of distributionally robust counterparts of stochastic optimal control problems. Randomized and non-randomized policies ar…
A Novel Catalyst Scheme for Stochastic Minimax Optimization
Guanghui Lan, Yan Li
This paper presents a proximal-point-based catalyst scheme for simple first-order methods applied to convex minimization and convex-concave minimax problems. In particular, for smo…
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms
Alexander Bukharin, Yan Li, Yue Yu +6
Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack robustness and are therefore sensitive…
Rectangularity and duality of distributionally robust Markov Decision Processes
Yan Li, Alexander Shapiro
The main goal of this paper is to discuss several approaches to formulation of distributionally robust counterparts of Markov Decision Processes, where the transition kernels are n…