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Found 5 papers, 4 papers with code
Learning-based adaption of robotic friction models
Subsequently, to adapt to more complex asymmetric settings, we train a second network on a small dataset, focusing on predicting the residual of the initial network's output.
ParFam -- Symbolic Regression Based on Continuous Global Optimization
In this paper, we present our new method ParFam that utilizes parametric families of suitable symbolic functions to translate the discrete symbolic regression problem into a continuous one, resulting in a more straightforward setup compared to current state-of-the-art methods.
Well-definedness of Physical Law Learning: The Uniqueness Problem
Finally, we provide extensive numerical experiments showing that our algorithms in combination with common approaches for learning physical laws indeed allow to guarantee that a unique governing differential equation is learnt, without assuming any knowledge about the function, thereby ensuring reliability.
Safe Policy Improvement Approaches and their Limitations
Based on this finding, we develop adaptations, the Adv-Soft-SPIBB algorithms, and show that they are provably safe.
Safe Policy Improvement Approaches on Discrete Markov Decision Processes
Safe Policy Improvement (SPI) aims at provable guarantees that a learned policy is at least approximately as good as a given baseline policy.
