2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 2 cited
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints
Ming Shi, Yingbin Liang, Ness Shroff
In many applications of Reinforcement Learning (RL), it is critically important that the algorithm performs safely, such that instantaneous hard constraints are satisfied at each s…
cs.LG2023
Near-Optimal Adversarial Reinforcement Learning with Switching Costs
Ming Shi, Yingbin Liang, Ness Shroff
Switching costs, which capture the costs for changing policies, are regarded as a critical metric in reinforcement learning (RL), in addition to the standard metric of losses (or r…