AI RESEARCH
Data-driven Acceleration of MPC with Guarantees
arXiv CS.AI
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ArXi:2511.13588v2 Announce Type: replace-cross Model Predictive Control (MPC) is a powerful framework for optimal control but can be too slow for low-latency applications. We present a data-driven framework to accelerate MPC by replacing online optimization with a nonparametric policy constructed from offline MPC solutions. Our policy is greedy with respect to a constructed upper bound on the optimal cost-to-go, and can be implemented as a nonparametric lookup rule that is orders of magnitude faster than solving MPC online.