The neural operator has emerged as a powerful tool in learning mappings
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A long-standing goal of reinforcement learning is that algorithms can le...
Learning partial differential equations' (PDEs) solution operators is an...
Recent advances of data-driven machine learning have revolutionized fiel...
We present a unified hard-constraint framework for solving geometrically...
In offline reinforcement learning, weighted regression is a common metho...
Deep learning based approaches like Physics-informed neural networks (PI...
Importance sampling (IS) is a popular technique in off-policy evaluation...
Embodied agents in vision navigation coupled with deep neural networks h...
Though deep reinforcement learning (DRL) has obtained substantial succes...
Certified defenses such as randomized smoothing have shown promise towar...
Recent works demonstrate that deep reinforcement learning (DRL) models a...