Effectively leveraging large, previously collected datasets in reinforcement learning (RL) is a key challenge for large-scale real-world applications. Offline RL algorithms promise to learn effective policies from previously-collected, static datasets without further interaction.

These disagreements raise the question: which, if any, of these dissimilarity measures should we believe?

In the case of coupled skew tent maps, theproposedmethodconsistently outperforms afivelayerDeepNeuralNetwork (DNN) and Long Short Term Memory (LSTM) architecture for unidirectional coupling coefficient values ranging from0.1 to 0.7.

Inversionoflinearsystems As mentioned in Corollary(1), for the Lasso, when computing the gradient off, one can either invert an nlinear system or anm mlinear system.

State of the art approaches are more efficient but typically rely on specific coordinate pruning schemes.