Apr 22, 2022 · In this work, we introduce the wavelet basis for reinforcement learning. Wavelets can effectively be used as a fixed basis and additionally ...

Using function approximation to represent a value function is necessary for continuous and high-dimensional state spaces. Linear function approximation has ...

Apr 22, 2022 · In this work, we introduce the wavelet basis for reinforcement learning. Wavelets can effectively be used as a fixed basis and additionally ...

Apr 22, 2022 · Using function approximation to represent a value function is necessary for continuous and high-dimensional state spaces.

Reinforcement learning agents solve tasks by finding policies that maximise their reward over time. The policy can be found from the value function, ...

Jul 30, 2015 · The multiscale adaptive wavelet approximation algorithm is an online approximation of an orthogonal matching pursuit algorithm. Proof. Since ...

We consider all of the wavelets (as opposed to standard truncation of small values), and simply subtract the measured variance from the wavelet coefficient.

Sep 27, 2022 · An adaptive wavelet-based collocation approach is studied in this contribution. It is shown that the Hill–Mandel energy equivalence condition can naturally be ...

Feb 18, 2023 · In this study, the applicability of physics informed neural networks using wavelets as an activation function is discussed to solve non-linear differential ...

This contribution focuses on the development of an adaptive hierarchical FFT-based approach for the efficient solution of microscale boundary value problems.