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

The paper frames the work as reinforcement learningImitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards.. Start here because it defines what success means and which assumptions the rest of the method inherits.

This systematic review surveys how reinforcement learningImitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. and model predictive controlControl & PlanningModel Predictive Control (MPC)A control method that repeatedly plans a short future path, acts a little, then replans. can be combined to build adaptive controllers that handle constraints and uncertainty—useful for developers building robotCore ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. controllers that need to optimize performance while respecting physical limits. The taxonomy and design patterns help you understand when to use RLImitation & Reinforcement LearningReinforcement Learning (RL)Teaching a robot through trial and error using rewards. for learning, when to use MPCControl & PlanningModel Predictive Control (MPC)A control method that repeatedly plans a short future path, acts a little, then replans. for safety, and how to combine them effectively. When reading the method section, identify the inputs, the learned or engineered representation, and the actionCore ConceptsActionA command the robot sends to its motors, controller, or low-level system. or prediction produced by the system.

For robotics work, the data story is part of the method: check whether the system depends on teleoperationImitation & Reinforcement LearningTeleoperation (teleop)A human remotely controlling the robot, often to collect demonstrations., simulationSimulation & Sim-to-RealSimulationA virtual environment where robots can be trained or tested., internet video, human labels, or robotCore ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. rollouts.

The paper should be judged through its evaluationSimulation & Sim-to-RealEvaluationMeasuring how well a robot system performs. protocol: what data is used, what robotCore ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. or simulator is tested, and which baselineEvaluation & ResearchBaselineA reference method used for comparison. comparisons support the claim. Look for the gap between the headline result and the deploymentSimulation & Sim-to-RealDeploymentPutting the trained system on a real robot. setting you would actually care about.