17 papers
Foundations of Reinforcement Learning and Control:Connections and New Perspectives
Claire Vernade, Onno Eberhard, Martha White +4
Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields…
Chess on Ice: Curling Tactical Decision-Making via Backward Induction and Deep Reinforcement Learning
Patrick Oberlin, Matteo Cederle, Aren Karapetyan +3
Curling is often referred to as "Chess on Ice", owing to the tactical complexity of its decision-making process. Yet unlike chess, curling remains largely underexplored from a mach…
Optimal Functional Incentives for Control: The Linear-Quadratic Case with Bilinear Incentives
Jonas G. Matt, Saverio Bolognani, Florian Dörfler
We study the design of functional incentive mechanisms for dynamical systems, in which a leader designs a fixed incentive function to motivate a self-interested follower to actuate…
On the Effect of Quadratic Regularization in Direct Data-Driven LQR
Manuel Klädtke, Feiran Zhao, Florian Dörfler +1
This paper proposes an explainability concept for direct data-driven linear quadratic regulation (LQR) with quadratic regularization. Our perspective follows the parametric effect…
A Bayesian Perspective on the Data-Driven LQR
Thierry Schwaller, Feiran Zhao, Florian Dörfler
The data-driven linear quadratic regulator (ddLQR) is a widely studied control method for unknown dynamical systems with disturbance. Existing approaches, both indirect, i.e., thos…
Scaled Graph Containment for Feedback Stability: Soft-Hard Equivalence and Conic Regions
Eder Baron-Prada, Julius P. J. Krebbekx, Adolfo Anta +1
Scaled graphs (SGs) offer a geometric framework for feedback stability analysis. This paper develops containment conditions for SGs within multiplier-defined regions, addressing bo…