On Convex Data-Driven Inverse Optimal Control for Nonlinear, Non-stationary and Stochastic Systems

• URL: http://arxiv.org/abs/2306.13928v2
• Date: Wed, 26 Jun 2024 17:59:37 GMT
• Title: On Convex Data-Driven Inverse Optimal Control for Nonlinear, Non-stationary and Stochastic Systems
• Authors: Emiland Garrabe, Hozefa Jesawada, Carmen Del Vecchio, Giovanni Russo,
• Abstract summary: This paper is concerned with a finite-horizon inverse control problem, which has the goal of reconstructing, from observations, the possibly non-nonstationary cost driving the actions of an agent. We present a result enabling cost optimization by solving an problem that is convex non-stationary agent cost. All experiments confirm the effectiveness of our approach.
• Score: 0.7240153598817866
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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