173 citations · 236 across the 22 of their papers we have counts for
41 papers
Sample Complexity of Variance-reduced Distributionally Robust Q-learning
Shengbo Wang, Nian Si, Jose Blanchet +1
Dynamic decision-making under distributional shifts is of fundamental interest in theory and applications of reinforcement learning: The distribution of the environment in which th…
Stochastic Nonsmooth Convex Optimization with Heavy-Tailed Noises: High-Probability Bound, In-Expectation Rate and Initial Distance Adaptation
Zijian Liu, Zhengyuan Zhou
Recently, several studies consider the stochastic optimization problem but in a heavy-tailed noise regime, i.e., the difference between the stochastic gradient and the true gradien…
A Finite Sample Complexity Bound for Distributionally Robust Q-learning
Shengbo Wang, Nian Si, Jose Blanchet +1
We consider a reinforcement learning setting in which the deployment environment is different from the training environment. Applying a robust Markov decision processes formulation…
Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise
Zijian Liu, Jiawei Zhang, Zhengyuan Zhou
We consider the stochastic optimization problem with smooth but not necessarily convex objectives in the heavy-tailed noise regime, where the stochastic gradient's noise is assumed…
Near-Optimal Non-Convex Stochastic Optimization under Generalized Smoothness
Zijian Liu, Srikanth Jagabathula, Zhengyuan Zhou
The generalized smooth condition, -smoothness, has triggered people's interest since it is more realistic in many optimization problems shown by both empirical and t…
Single-Trajectory Distributionally Robust Reinforcement Learning
Zhipeng Liang, Xiaoteng Ma, Jose Blanchet +2
To mitigate the limitation that the classical reinforcement learning (RL) framework heavily relies on identical training and test environments, Distributionally Robust RL (DRRL) ha…