Reinforcement Learning of the Prediction Horizon in Model Predictive Control

by   Eivind Bøhn, et al.

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.


page 1

page 2

page 3

page 4


Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning

Model predictive control (MPC) is increasingly being considered for cont...

Approximate Robust NMPC using Reinforcement Learning

We present a Reinforcement Learning-based Robust Nonlinear Model Predict...

Predict-and-Critic: Accelerated End-to-End Predictive Control for Cloud Computing through Reinforcement Learning

Cloud computing holds the promise of reduced costs through economies of ...

Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle Relocation and Pricing Decisions

Large-scale ride-hailing systems often combine real-time routing at the ...

Contingency Model Predictive Control for Automated Vehicles

We present Contingency Model Predictive Control (CMPC), a novel and impl...

Combining model-predictive control and predictive reinforcement learning for stable quadrupedal robot locomotion

Stable gait generation is a crucial problem for legged robot locomotion ...

Contingency Model Predictive Control for Linear Time-Varying Systems

We present Contingency Model Predictive Control (CMPC), a motion plannin...

Please sign up or login with your details

Forgot password? Click here to reset