End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control

arXiv:2308.01674v3 Announce Type: replace
Abstract: (Economic) nonlinear model predictive control ((e)NMPC) requires dynamic models that are sufficiently accurate and computationally tractable. Data-driven surrogate models for mechanistic models can reduce the computational burden of (e)NMPC; however, such models are typically trained by system identification for maximum prediction accuracy on simulation samples and perform suboptimally in (e)NMPC. We present a method for end-to-end reinforcement learning of Koopman surrogate models for optimal performance as part of (e)NMPC. We apply our method to two …

abstract accuracy arxiv computational control cs.lg cs.sy data data-driven dynamic economic eess.sy however identification nonlinear model prediction predictive reduce reinforcement reinforcement learning tractable type