On Architectures for Combining Reinforcement Learning and Model Predictive Control with Runtime Improvements

Xiaolong Jia; Nikhil Bajaj

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arXiv · 2510.03354

On Architectures for Combining Reinforcement Learning and Model Predictive Control with Runtime Improvements

Xiaolong Jia·Nikhil Bajaj

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Abstract

Model Predictive Control (MPC) faces computational demands and performance degradation from model inaccuracies. We propose two architectures combining Neural Network-approximated MPC (NNMPC) with Reinforcement Learning (RL). The first, Warm Start RL, initializes the RL actor with pre-trained NNMPC weights. The second, RLMPC, uses RL to generate corrective residuals for NNMPC outputs. We introduce a downsampling method reducing NNMPC input dimensions while maintaining performance. Evaluated on a rotary inverted pendulum, both architectures demonstrate runtime reductions exceeding 99% compared to traditional MPC while improving tracking performance under model uncertainties, with RL+MPC achieving 11-40% cost reduction depending on reference amplitude.

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BibTeX RIS

Xiaolong Jia, Nikhil Bajaj. 2025-10-02. On Architectures for Combining Reinforcement Learning and Model Predictive Control with Runtime Improvements. https://arxiv.org/abs/2510.03354

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