12 papers · 1 filter
Timescale Separation Through the Lens of Operator Theory
Guido Carnevale, Nicola Bastianello, Luca Schenato +2
Timescale separation is a powerful tool for analyzing interconnected dynamical systems. Meanwhile, operator theory provides a general framework for studying the convergence of iter…
Suboptimal and Reduced-Order MPC via Timescale Separation
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
In this paper, we propose a generalized framework for the design and analysis of suboptimal and reduced-order nonlinear Model Predictive Control (MPC) architectures. The proposed f…
On Reward-Balancing Methods for Reinforcement Learning
Simone Baroncini, Bahman Gharesifard, Giuseppe Notarstefano
This paper investigates the so-called reward-balancing methods, a novel class of algorithms for solving discounted-return reinforcement learning (RL) problems. These methods consis…
A Distributed Bilevel Framework for the Macroscopic Optimization of Multi-Agent Systems
Riccardo Brumali, Guido Carnevale, Sonia MartÃnez +1
In this paper, we propose a novel distributed algorithm to optimize the emergent macroscopic behavior of large-scale multi-agent systems via microscopic actions. We cast this task…
Model-Free Aggregative Cooperative Optimization via Randomized Gradient-Free Minimization and Exploration Momentum
Amir Mehrnoosh, Giuseppe Speciale, Riccardo Brumali +2
Aggregative cooperative optimization problems arise in distributed decision-making settings where each agent's objective depends on its own decision as well as on an aggregate vari…
Safe Control of Feedback-Interconnected Systems via Singular Perturbations
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
Control Barrier Functions (CBFs) have emerged as a powerful tool in the design of safety-critical controllers for nonlinear systems. In modern applications, complex systems often i…