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- W4382319907 abstract "This paper is concerned with a finite-horizon inverse control problem, which has the goal of inferring, from observations, the possibly non-convex and non-stationary cost driving the actions of an agent. In this context, we present a result that enables cost estimation 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. For this problem, we give an explicit expression for the optimal solution. Moreover, we turn our findings into algorithmic procedures and we show the effectiveness of our approach via both in-silico and experimental validations with real hardware. All the experiments confirm the effectiveness of our approach." @default.
- W4382319907 created "2023-06-28" @default.
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- W4382319907 date "2023-06-24" @default.
- W4382319907 modified "2023-09-25" @default.
- W4382319907 title "On Convex Data-Driven Inverse Optimal Control for Nonlinear, Non-stationary and Stochastic Systems" @default.
- W4382319907 doi "https://doi.org/10.48550/arxiv.2306.13928" @default.
- W4382319907 hasPublicationYear "2023" @default.
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