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Found 8 papers, 3 papers with code
Robust Deep Reinforcement Learning through Adversarial Loss
To address this issue, we propose RADIAL-RL, a principled framework to train reinforcement learning agents with improved robustness against $l_p$-norm bounded adversarial attacks.
One step closer to unbiased aleatoric uncertainty estimation
Neural networks are powerful tools in various applications, and quantifying their uncertainty is crucial for reliable decision-making.
Convex Parameterizations and Fidelity Bounds for Nonlinear Identification and Reduced-Order Modelling
Model instability and poor prediction of long-term behavior are common problems when modeling dynamical systems using nonlinear "black-box" techniques.
Robust Online Control with Model Misspecification
We study online control of an unknown nonlinear dynamical system that is approximated by a time-invariant linear system with model misspecification.
Efficient Certification for Probabilistic Robustness
Recent developments on the robustness of neural networks have primarily emphasized the notion of worst-case adversarial robustness in both verification and robust training.
Tactics on Refining Decision Boundary for Improving Certification-based Robust Training
In verification-based robust training, existing methods utilize relaxation based methods to bound the worst case performance of neural networks given certain perturbation.
Mathematical certification of motion planning on uncertain terrain with limited perception: a case study
We design a controller for an agent whose mission is to reach a stationary target while avoiding a family of obstacles which are not known a-priori.
ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction
Deep neural networks (DNN) have shown great capacity of modeling a dynamical system; nevertheless, they usually do not obey physics constraints such as conservation laws.
