  Model predictive control (MPC) is a powerful trajectory optimization control
technique capable of controlling complex nonlinear systems while respecting
system constraints and ensuring safe operation. The MPC's capabilities come at
the cost of a high online computational complexity, the requirement of an
accurate model of the system dynamics, and the necessity of tuning its
parameters to the specific control application. The main tunable parameter
affecting the computational complexity is the prediction horizon length,
controlling how far into the future the MPC predicts the system response and
thus evaluates the optimality of its computed trajectory. A longer horizon
generally increases the control performance, but requires an increasingly
powerful computing platform, excluding certain control applications.The
performance sensitivity to the prediction horizon length varies over the state
space, and this motivated the adaptive horizon model predictive control
(AHMPC), which adapts the prediction horizon according to some criteria. In
this paper we propose to learn the optimal prediction horizon as a function of
the state using reinforcement learning (RL). We show how the RL learning
problem can be formulated and test our method on two control tasks, showing
clear improvements over the fixed horizon MPC scheme, while requiring only
minutes of learning.
