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Found 5 papers, 2 papers with code
Regret-Optimal Control under Partial Observability
This paper studies online solutions for regret-optimal control in partially observable systems over an infinite-horizon.
Optimal Competitive-Ratio Control
The key techniques that underpin our explicit solution is a reduction of the control problem to a Nehari problem, along with a novel factorization of the clairvoyant controller's cost.
Regret-Optimal LQR Control
Motivated by competitive analysis in online learning, as a criterion for controller design we introduce the dynamic regret, defined as the difference between the LQR cost of a causal controller (that has only access to past disturbances) and the LQR cost of the \emph{unique} clairvoyant one (that has also access to future disturbances) that is known to dominate all other controllers.
Regret-Optimal Filtering for Prediction and Estimation
For the important case of signals that can be described with a time-invariant state-space, we provide an explicit construction for the regret optimal filter in the estimation (causal) and the prediction (strictly-causal) regimes.
Computing the Feedback Capacity of Finite State Channels using Reinforcement Learning
In this paper, we propose a novel method to compute the feedback capacity of channels with memory using reinforcement learning (RL).
