27 citations · 93 across the 17 of their papers we have counts for
17 papers
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang +6
Reinforcement learning (RL) is challenging in the common case of delays between events and their sensory perceptions. State-of-the-art (SOTA) state augmentation techniques either s…
State-Wise Safe Reinforcement Learning With Pixel Observations
Simon Sinong Zhan, Yixuan Wang, Qingyuan Wu +3
In the context of safe exploration, Reinforcement Learning (RL) has long grappled with the challenges of balancing the tradeoff between maximizing rewards and minimizing safety vio…
POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems
Yixuan Wang, Weichao Zhou, Jiameng Fan +6
Neural networks (NNs) playing the role of controllers have demonstrated impressive empirical performances on challenging control problems. However, the potential adoption of NN con…
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
Yixuan Wang, Simon Sinong Zhan, Ruochen Jiao +6
It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an unknown and stochastic environment under hard constraints that require the system state not…
A Tool for Neural Network Global Robustness Certification and Training
Zhilu Wang, Yixuan Wang, Feisi Fu +4
With the increment of interest in leveraging machine learning technology in safety-critical systems, the robustness of neural networks under external disturbance receives more and…
Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding
Zhilu Wang, Chao Huang, Qi Zhu
The robustness of deep neural networks has received significant interest recently, especially when being deployed in safety-critical systems, as it is important to analyze how sens…
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning
Yixuan Wang, Simon Zhan, Zhilu Wang +4
In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties (e.g., safety, stability) under the learned control…
Verification in the Loop: Correct-by-Construction Control Learning with Reach-avoid Guarantees
Yixuan Wang, Chao Huang, Zhaoran Wang +2
In the current control design of safety-critical autonomous systems, formal verification techniques are typically applied after the controller is designed to evaluate whether the r…
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems
Chao Huang, Jiameng Fan, Zhilu Wang +6
We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs). Existing approaches that le…
Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation
Yixuan Wang, Chao Huang, Zhilu Wang +3
Neural networks are being increasingly applied to control and decision-making for learning-enabled cyber-physical systems (LE-CPSs). They have shown promising performance without r…
Leveraging Weakly-hard Constraints for Improving System Fault Tolerance with Functional and Timing Guarantees
Hengyi Liang, Zhilu Wang, Ruochen Jiao +1
Many safety-critical real-time systems operate under harsh environment and are subject to soft errors caused by transient or intermittent faults. It is critical and yet often very…
Distributed Multi-agent Video Fast-forwarding
Shuyue Lan, Zhilu Wang, Amit K. Roy-Chowdhury +2
In many intelligent systems, a network of agents collaboratively perceives the environment for better and more efficient situation awareness. As these agents often have limited res…
Energy-Efficient Control Adaptation with Safety Guarantees for Learning-Enabled Cyber-Physical Systems
Yixuan Wang, Chao Huang, Qi Zhu
Neural networks have been increasingly applied for control in learning-enabled cyber-physical systems (LE-CPSs) and demonstrated great promises in improving system performance and…
Cross-Layer Design of Automotive Systems
Zhilu Wang, Hengyi Liang, Chao Huang +1
With growing system complexity and closer cyber-physical interaction, there are increasingly stronger dependencies between different function and architecture layers in automotive…
SAW: A Tool for Safety Analysis of Weakly-hard Systems
Chao Huang, Kai-Chieh Chang, Chung-Wei Lin +1
We introduce SAW, a tool for safety analysis of weakly-hard systems, in which traditional hard timing constraints are relaxed to allow bounded deadline misses for improving design…
Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems
Chao Huang, Shichao Xu, Zhilu Wang +3
Control schemes for autonomous systems are often designed in a way that anticipates the worst case in any situation. At runtime, however, there could exist opportunities to leverag…
ReachNN: Reachability Analysis of Neural-Network Controlled Systems
Chao Huang, Jiameng Fan, Wenchao Li +2
Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neu…