Model Predictive Control (MPC) in Reinforcement Learning (RL) refers to a method that combines control theory with machine learning to create a strategy for making decisions over time. At its core, MPC involves creating a model of the system or environment that can predict future states based on current actions. This model is then used to optimize the control inputs by calculating the best actions to take at each decision point while considering future consequences.
In practical terms, MPC works by solving an optimization problem at each time step. Developers start by developing a predictive model of the system, which can capture the dynamics and how different actions affect future states. For example, in a robotics scenario, the model could predict the robot's position based on its current speed and direction. The MPC then uses this model to simulate a series of potential future actions over a defined time horizon and evaluates them based on a cost or reward function that represents the system's objectives, such as minimizing energy consumption or maximizing performance.
One advantage of incorporating MPC in RL is that it can lead to more stable and efficient learning, especially in environments where decision-making needs to be adaptive. For instance, in autonomous driving, an MPC controller can plan the path for the vehicle while considering traffic conditions, road constraints, and the target destination. By continuously updating its model with new data, MPC can improve its predictions and make better decisions, resulting in a more informed approach to navigating complex environments. This combination of predictive modeling and optimization makes MPC a powerful tool in the realm of control and decision-making.