14 citations · 24 across the 9 of their papers we have counts for
6 papers · 1 filter
Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning
Honghao Wei, Xiyue Peng, Arnob Ghosh +1
We propose WSAC (Weighted Safe Actor-Critic), a novel algorithm for Safe Offline Reinforcement Learning (RL) under functional approximation, which can robustly optimize policies to…
Safe Reinforcement Learning with Instantaneous Constraints: The Role of Aggressive Exploration
Honghao Wei, Xin Liu, Lei Ying
This paper studies safe Reinforcement Learning (safe RL) with linear function approximation and under hard instantaneous constraints where unsafe actions must be avoided at each st…
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
Honghao Wei, Xin Liu, Weina Wang +1
This paper considers a class of reinforcement learning problems, which involve systems with two types of states: stochastic and pseudo-stochastic. In such systems, stochastic state…
Online Nonstochastic Control with Adversarial and Static Constraints
Xin Liu, Zixian Yang, Lei Ying
This paper studies online nonstochastic control problems with adversarial and static constraints. We propose online nonstochastic control algorithms that achieve both sublinear reg…
A Provably-Efficient Model-Free Algorithm for Constrained Markov Decision Processes
Honghao Wei, Xin Liu, Lei Ying
This paper presents the first model-free, simulator-free reinforcement learning algorithm for Constrained Markov Decision Processes (CMDPs) with sublinear regret and zero constrain…
An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints
Xin Liu, Bin Li, Pengyi Shi +1
This paper considers stochastic linear bandits with general nonlinear constraints. The objective is to maximize the expected cumulative reward over horizon subject to a set of…