Model Predictive Control (MPC) in Reinforcement Learning (RL) is a control strategy that uses a dynamic model of a system to predict future states and optimize actions over a finite time horizon. Unlike traditional RL methods, which often learn policies through trial and error without explicit models, MPC repeatedly solves an optimization problem at each step to select actions. For example, in robot navigation, MPC might predict the robot’s position 5 seconds ahead, compute the best steering and velocity adjustments to avoid obstacles, execute the first action, then replan with updated sensor data. This approach combines the predictive power of models with the adaptability of online optimization, making it suitable for systems with constraints like safety limits or energy budgets.
MPC operates in three key steps: prediction, optimization, and receding horizon execution. First, the system’s model (either learned via RL or known analytically) predicts future states for a fixed horizon (e.g., 10 steps). Next, an optimizer identifies the action sequence that minimizes a cost function (e.g., tracking error) while satisfying constraints (e.g., joint torque limits). Only the first action is executed, and the process repeats with the latest state. For instance, an autonomous car might use MPC to plan a trajectory that minimizes braking effort while staying within lane boundaries, updating its plan every 0.1 seconds as traffic changes. The reliance on real-time optimization allows MPC to handle dynamic environments but requires efficient solvers (e.g., quadratic programming libraries) to meet latency requirements.
MPC is widely used in RL for applications demanding real-time adaptability and constraint handling. Industrial robots, for example, use MPC to adjust grip force in assembly tasks based on predictions of object slippage. A key trade-off is computational cost: while MPC’s frequent replanning improves responsiveness, it demands fast models and optimizers. Developers often balance horizon length (longer horizons improve foresight but increase compute time) and model accuracy (simplified models speed up predictions but may sacrifice fidelity). Compared to model-free RL, which trains policies offline, MPC excels in scenarios where the environment changes unpredictably or constraints must be enforced rigorously. However, integrating MPC into RL pipelines requires careful tuning of the model, cost function, and solver settings to align with system capabilities.