4 citations · 12 across the 12 of their papers we have counts for
6 papers · 1 filter
Verified Safe Reinforcement Learning for Neural Network Dynamic Models
Junlin Wu, Huan Zhang, Yevgeniy Vorobeychik
Learning reliably safe autonomous control is one of the core problems in trustworthy autonomy. However, training a controller that can be formally verified to be safe remains a maj…
Preference Poisoning Attacks on Reward Model Learning
Junlin Wu, Jiongxiao Wang, Chaowei Xiao +3
Learning reward models from pairwise comparisons is a fundamental component in a number of domains, including autonomous control, conversational agents, and recommendation systems,…
Exact Verification of ReLU Neural Control Barrier Functions
Hongchao Zhang, Junlin Wu, Yevgeniy Vorobeychik +1
Control Barrier Functions (CBFs) are a popular approach for safe control of nonlinear systems. In CBF-based control, the desired safety properties of the system are mapped to nonne…
Neural Lyapunov Control for Discrete-Time Systems
Junlin Wu, Andrew Clark, Yiannis Kantaros +1
While ensuring stability for linear systems is well understood, it remains a major challenge for nonlinear systems. A general approach in such cases is to compute a combination of…
Certifying Safety in Reinforcement Learning under Adversarial Perturbation Attacks
Junlin Wu, Hussein Sibai, Yevgeniy Vorobeychik
Function approximation has enabled remarkable advances in applying reinforcement learning (RL) techniques in environments with high-dimensional inputs, such as images, in an end-to…
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum
Junlin Wu, Yevgeniy Vorobeychik
Despite considerable advances in deep reinforcement learning, it has been shown to be highly vulnerable to adversarial perturbations to state observations. Recent efforts that have…