27 citations · 89 across the 18 of their papers we have counts for
7 papers · 1 filter
Variational Delayed Policy Optimization
Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang +5
In environments with delayed observation, state augmentation by including actions within the delay window is adopted to retrieve Markovian property to enable reinforcement learning…
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…
Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling
Ruochen Jiao, Yixuan Wang, Xiangguo Liu +2
Trajectory generation and trajectory prediction are two critical tasks in autonomous driving, which generate various trajectories for testing during development and predict the tra…
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…