Distributed nonlinear model predictive control and reinforcement learning

Abstract

Coordinating two or more dynamic systems such as autonomous vehicles or satellites in a distributed manner poses an important research challenge. Multiple approaches to this problem have been proposed including Nonlinear Model Predictive Control (NMPC) and its model-free counterparts in reinforcement learning (RL) literature such as Deep QNetwork (DQN). This initial study aims to compare and contrast the optimal control technique, NMPC, where the model is known, with the popular model-free RL method, DQN. Simple distributed variants of these for the specific problem of balancing and synchronising two highly unstable cart-pole systems are investigated numerically. We found that both NMPC and ..