6 citations · 18 across the 14 of their papers we have counts for
15 papers
Boundary-Seeking Policy Gradient for Safe Reinforcement Learning
Chenhua Fan, Jiahui Zhu, Yuhang Zhang +1
Safe reinforcement learning maximizes reward subject to safety constraints. For Constrained Markov Decision Processes, the linear-programming view over occupancy measures implies t…
Robust Peak-cost Constrained Reinforcement Learning
Shilpa Mukhopadhyay, Sourav Ganguly, Santosh Mohan Rajkumar +3
We study robust peak-cost constrained reinforcement learning (\ours), where the objective is to maximize expected reward while controlling the maximum cost encountered along a traj…
An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints
Jiahui Zhu, Kihyun Yu, Dabeen Lee +2
Online safe reinforcement learning (RL) plays a key role in dynamic environments, with applications in autonomous driving, robotics, and cybersecurity. The objective is to learn op…
Enhancing Safety in Reinforcement Learning with Human Feedback via Rectified Policy Optimization
Xiyue Peng, Hengquan Guo, Jiawei Zhang +4
Balancing helpfulness and safety (harmlessness) is a critical challenge in aligning large language models (LLMs). Current approaches often decouple these two objectives, training s…
Reinforcement Learning from Human Feedback without Reward Inference: Model-Free Algorithm and Instance-Dependent Analysis
Qining Zhang, Honghao Wei, Lei Ying
In this paper, we study reinforcement learning from human feedback (RLHF) under an episodic Markov decision process with a general trajectory-wise reward model. We developed a mode…
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…
Model-Free, Regret-Optimal Best Policy Identification in Online CMDPs
Zihan Zhou, Honghao Wei, Lei Ying
This paper considers the best policy identification (BPI) problem in online Constrained Markov Decision Processes (CMDPs). We are interested in algorithms that are model-free, have…
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…
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs
Honghao Wei, Arnob Ghosh, Ness Shroff +2
We study model-free reinforcement learning (RL) algorithms in episodic non-stationary constrained Markov Decision Processes (CMDPs), in which an agent aims to maximize the expected…
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems
Xin Liu, Honghao Wei, Lei Ying
This paper studies a class of multi-agent reinforcement learning (MARL) problems where the reward that an agent receives depends on the states of other agents, but the next state o…
On Low-Complexity Quickest Intervention of Mutated Diffusion Processes Through Local Approximation
Qining Zhang, Honghao Wei, Weina Wang +1
We consider the problem of controlling a mutated diffusion process with an unknown mutation time. The problem is formulated as the quickest intervention problem with the mutation m…
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…
FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning
Honghao Wei, Lei Ying
In this paper, we propose a new type of Actor, named forward-looking Actor or FORK for short, for Actor-Critic algorithms. FORK can be easily integrated into a model-free Actor-Cri…
QuickStop: A Markov Optimal Stopping Approach for Quickest Misinformation Detection
Honghao Wei, Xiaohan Kang, Weina Wang +1
This paper combines data-driven and model-driven methods for real-time misinformation detection. Our algorithm, named QuickStop, is an optimal stopping algorithm based on a probabi…