16 citations · 21 across the 5 of their papers we have counts for
5 papers
Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
Ruida Zhou, Tao Liu, Min Cheng +3
We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testi…
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
Ruida Zhou, Tao Liu, Dileep Kalathil +2
We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as propor…
Approximate Top- Arm Identification with Heterogeneous Reward Variances
Ruida Zhou, Chao Tian
We study the effect of reward variance heterogeneity in the approximate top- arm identification setting. In this setting, the reward for the -th arm follows a -sub-Gau…
Policy Optimization for Constrained MDPs with Provable Fast Global Convergence
Tao Liu, Ruida Zhou, Dileep Kalathil +2
We address the problem of finding the optimal policy of a constrained Markov decision process (CMDP) using a gradient descent-based algorithm. Previous results have shown that a pr…
Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs
Tao Liu, Ruida Zhou, Dileep Kalathil +2
We address the issue of safety in reinforcement learning. We pose the problem in an episodic framework of a constrained Markov decision process. Existing results have shown that it…