ARXIV:2604.21030 · REINFORCEMENT LEARNING · SUBMITTED 24 APR · 20:33 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems

Mohsen Jalaeian Farimani · Roya Khalili Amirabadi · Davoud Nikkhouy · Malihe Abdolbaghi · Mahshad Rastegarmoghaddam · Shima Samadzadeh · arXiv

A systematic review and taxonomy of integrating Reinforcement Learning with Model Predictive Control for linear systems.

Blocked on Code›Score2.0Evidence unverified

Opportunity summary

Pain A systematic review and taxonomy of integrating Reinforcement Learning with Model Predictive Control for linear systems.

Evidence 0 refs | 3 sources | 50% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A systematic review and taxonomy of integrating Reinforcement Learning with Model Predictive Control for linear systems. MPC offers structured optimization, explicit constraint handling, and established stability tools, whereas RL provides data-driven adaptation and performance…

METHOD

Full abstract

The integration of Model Predictive Control (MPC) and Reinforcement Learning (RL) has emerged as a promising paradigm for constrained decision-making and adaptive control. MPC offers structured optimization, explicit constraint handling, and established stability tools, whereas RL provides data-driven adaptation and performance improvement in the presence of uncertainty and model mismatch. Despite the rapid growth of research on RL--MPC integration, the literature remains fragmented, particularly for control architectures built on linear or linearized predictive models. This paper presents a comprehensive Systematic Literature Review (SLR) of RL--MPC integrations for linear and linearized systems, covering peer-reviewed and formally indexed studies published until 2025. The reviewed studies are organized through a multi-dimensional taxonomy covering RL functional roles, RL algorithm classes, MPC formulations, cost-function structures, and application domains. In addition, a cross-dimensional synthesis is conducted to identify recurring design patterns and reported associations among these dimensions within the reviewed corpus. The review highlights methodological trends, commonly adopted integration strategies, and recurring practical challenges, including computational burden, sample efficiency, robustness, and closed-loop guarantees. The resulting synthesis provides a structured reference for researchers and practitioners seeking to design or analyze RL--MPC architectures based on linear or linearized predictive control formulations.

RESULT

ScienceToStartup currently rates this 2.0/10 on the public viability pass. The resulting synthesis provides a structured reference for researchers and practitioners seeking to design or analyze RL--MPC architectures based on linear or linearized predictive…

WHY NOW

Reinforcement Learning moved forward this cycle; last verified April 2026. Public score 2.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score2.0

PainA systematic review and taxonomy of integrating Reinforcement Learning with Model Predictive Control for linear systems.

Evidence0 refs | 3 sources | 50% coverage

Blockerno shell-level blocker reported

Analysis summary

A systematic review and taxonomy of integrating Reinforcement Learning with Model Predictive Control for linear systems.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

A systematic review and taxonomy of integrating Reinforcement Learning with Model Predictive Control for linear systems.

Segment

Reinforcement Learning

Adoption evidence

No public code link in the paper record yet

Commercial read

2.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

{ "contract_version": "paper-r2", "paper_id": "187ea823-f0d2-4cc3-8852-1d4e3724e42a", "arxiv_id": "2604.21030", "canonical_route": "/paper/a-systematic-review-and-taxonomy-of-reinforcement-learning-model-predictive-control-integration-for-linear-systems", "active_tab": "synced from current hash by the drawer client", "selected_artifact": "a-systematic-review-and-taxonomy-of-reinforcement-learning-model-predictive-control-integration-for-linear-systems", "endpoints": { "paper_pack": "/api/v1/paper/a-systematic-review-and-taxonomy-of-reinforcement-learning-model-predictive-control-integration-for-linear-systems/paper-pack", "build_passport": "/api/v1/paper/a-systematic-review-and-taxonomy-of-reinforcement-learning-model-predictive-control-integration-for-linear-systems/build-passport", "mcp_resource": "sciencetostartup://surfaces/paper-workspace" } }

{ "surface": "paper", "mode": "paper", "query": "A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems", "normalized_query": "2604.21030", "route": "/paper/a-systematic-review-and-taxonomy-of-reinforcement-learning-model-predictive-control-integration-for-linear-systems", "paper_ref": "a-systematic-review-and-taxonomy-of-reinforcement-learning-model-predictive-control-integration-for-linear-systems", "topic_slug": null, "benchmark_ref": null, "dataset_ref": null }

{ "@context": "https://schema.org", "@graph": [ { "@type": "WebPage", "@id": "https://sciencetostartup.com/paper/a-systematic-review-and-taxonomy-of-reinforcement-learning-model-predictive-control-integration-for-linear-systems#webpage", "url": "https://sciencetostartup.com/paper/a-systematic-review-and-taxonomy-of-reinforcement-learning-model-predictive-control-integration-for-linear-systems", "name": "A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems", "description": "A systematic review and taxonomy of integrating Reinforcement Learning with Model Predictive Control for linear systems.", "isPartOf": { "@id": "https://sciencetostartup.com/#website" } }, { "@type": "ScholarlyArticle", "@id": "https://sciencetostartup.com/paper/a-systematic-review-and-taxonomy-of-reinforcement-learning-model-predictive-control-integration-for-linear-systems#scholarlyArticle", "headline": "A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems", "description": "A systematic review and taxonomy of integrating Reinforcement Learning with Model Predictive Control for linear systems.", "url": "https://sciencetostartup.com/paper/a-systematic-review-and-taxonomy-of-reinforcement-learning-model-predictive-control-integration-for-linear-systems", "sameAs": "https://arxiv.org/abs/2604.21030", "identifier": { "@type": "PropertyValue", "propertyID": "arXiv", "value": "2604.21030" }, "isAccessibleForFree": true, "isPartOf": { "@id": "https://sciencetostartup.com/#website" }, "datePublished": "2026-04-22T19:22:29.000Z", "author": [ { "@type": "Person", "name": "Mohsen Jalaeian Farimani" }, { "@type": "Person", "name": "Roya Khalili Amirabadi" }, { "@type": "Person", "name": "Davoud Nikkhouy" }, { "@type": "Person", "name": "Malihe Abdolbaghi" }, { "@type": "Person", "name": "Mahshad Rastegarmoghaddam" }, { "@type": "Person", "name": "Shima Samadzadeh" } ], "additionalProperty": [ { "@type": "PropertyValue", "propertyID": "viabilityScore", "value": 2 }, { "@type": "PropertyValue", "propertyID": "researchDomain", "value": "Reinforcement Learning" } ] }, { "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "Home", "item": "https://sciencetostartup.com" }, { "@type": "ListItem", "position": 2, "name": "Reinforcement Learning", "item": "https://sciencetostartup.com/topics" }, { "@type": "ListItem", "position": 3, "name": "A Systematic Review and Taxonomy of Reinforcement Learning-M", "item": "https://sciencetostartup.com/paper/a-systematic-review-and-taxonomy-of-reinforcement-learning-model-predictive-control-integration-for-linear-systems" } ] } ] }

Competitive landscape

A systematic review and taxonomy of integrating Reinforcement Learning with Model Predictive Control for linear systems.

Segment

Reinforcement Learning

Adoption evidence

No public code link in the paper record yet

Commercial read

2.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems

A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems

Claim map

Constellation map

Competitive landscape

Buzz

PDF

REFERENCES

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

REFERENCES

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline