2023/06/24 by Émiland Garrabé, Hozefa Jesawada, Garrabe, Emiland +5 · 3 citations
Computer Science · Engineering · #Advanced Control Systems Optimization #Distributed Control Multi-Agent Systems #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Optimization and Search Problems #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2306.13928
openalex publication_date 2023/06/24 · openalex created_date 2023/06/28 · openalex updated_date 2026/07/28
This paper is concerned with a finite-horizon inverse control problem, which has the goal of reconstructing, from observations, the possibly non-convex and non-stationary cost driving the actions of an agent. In this context, we present a result enabling cost reconstruction by solving an optimization problem that is convex even when the agent cost is not and when the underlying dynamics is nonlinear, non-stationary and stochastic. To obtain this result, we also study a finite-horizon forward control problem that has randomized policies as decision variables. We turn our findings into algorithmic procedures and show the effectiveness of our approach via in-silico and hardware validations. All experiments confirm the effectiveness of our approach.