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Found 7 papers, 3 papers with code
On the equivalence of direct and indirect data-driven predictive control approaches
Recently, several direct Data-Driven Predictive Control (DDPC) methods have been proposed, advocating the possibility of designing predictive controllers from historical input-output trajectories without the need to identify a model.
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
We show that such an SDE has a solution that we can use to calculate the log probability of the policy, yielding an entropy regularizer that improves the exploration of offline datasets.
Structured state-space models are deep Wiener models
The goal of this paper is to provide a system identification-friendly introduction to the Structured State-space Models (SSMs).
Aiding reinforcement learning for set point control
While reinforcement learning has made great improvements, state-of-the-art algorithms can still struggle with seemingly simple set-point feedback control problems.
Robust nonlinear set-point control with reinforcement learning
This paper argues that three ideas can improve reinforcement learning methods even for highly nonlinear set-point control problems: 1) Make use of a prior feedback controller to aid amplitude exploration.
Tuned Regularized Estimators for Linear Regression via Covariance Fitting
We start by showing that three known optimal linear estimators belong to a wider class of estimators that can be formulated as a solution to a weighted and constrained minimization problem.
Recursive nonlinear-system identification using latent variables
In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs.
