  Model predictive control (MPC) is a powerful technique for solving dynamic
control tasks. In this paper, we show that there exists a close connection
between MPC and online learning, an abstract theoretical framework for
analyzing online decision making in the optimization literature. This new
perspective provides a foundation for leveraging powerful online learning
algorithms to design MPC algorithms. Specifically, we propose a new algorithm
based on dynamic mirror descent (DMD), an online learning algorithm that is
designed for non-stationary setups. Our algorithm, Dynamic Mirror Descent Model
Predictive Control (DMD-MPC), represents a general family of MPC algorithms
that includes many existing techniques as special instances. DMD-MPC also
provides a fresh perspective on previous heuristics used in MPC and suggests a
principled way to design new MPC algorithms. In the experimental section of
this paper, we demonstrate the flexibility of DMD-MPC, presenting a set of new
MPC algorithms on a simple simulated cartpole and a simulated and real-world
aggressive driving task. Videos of the real-world experiments can be found at
https://youtu.be/vZST3v0_S9w and https://youtu.be/MhuqiHo2t98.
