Reinforcement Learning Control Meets MPC - But Four Challenges Remain
A new systematic literature review is tackling one of the more tangled corners of modern control engineering: how to combine Reinforcement Learning with Model Predictive Control in systems that behave, at least approximately, like linear ones. The paper, authored by Mohsen Jalaeian-Farimani, argues that despite years of growing interest in blending these two approaches, researchers still lack a clear map of what’s been tried, what works, and where the gaps sit. That’s the gap this review tries to close, particularly for what it calls model predictive control reinforcement architectures built around linear or linearized predictive models.
Key takeaways
- The review is a systematic literature review covering RL-MPC integration in linear and linearized systems, including peer-reviewed and formally indexed studies published up to 2025.
- Studies are sorted into a multi-dimensional taxonomy spanning RL functional roles, RL algorithm classes, MPC formulations, cost-function structures, and application domains.
- A cross-dimensional synthesis uncovers recurring design patterns and reported links between these categories.
- Recurring practical challenges include computational burden, sample efficiency, robustness, and closed-loop guarantees.
- The author frames the paper’s conclusions as a structured reference for researchers and practitioners designing or analyzing these architectures.
Systematic Review of RL-MPC Integration in Linear Systems
At its core, the review sets out to organize a fragmented body of research into something usable. It focuses specifically on how reinforcement learning and Model Predictive Control get paired together when the underlying predictive model is linear or has been linearized. That’s a deliberate scope choice — nonlinear integrations exist elsewhere in the literature, but this paper isolates the linear case to give it a dedicated, structured treatment.
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