Neural Flocking: MPC-based Supervised Learning of Flocking Controllers

TL;DR

This paper presents a method to train distributed neural flocking controllers using supervised learning from centralized MPC controllers, achieving high performance and efficiency in various flocking tasks.

Contribution

It introduces a novel approach to synthesize distributed neural flocking controllers from centralized MPC controllers, combining high performance with computational efficiency.

Findings

Neural controllers match MPC performance in flocking tasks.

Controllers generalize to broader scenarios beyond training data.

Achieve multiple flocking objectives like formation and obstacle avoidance.

Abstract

We show how a distributed flocking controller can be synthesized using deep learning from a centralized controller which generates the trajectories of the flock. Our approach is based on supervised learning, with the centralized controller providing the training data to the learning agent, i.e., the synthesized distributed controller. We use Model Predictive Control (MPC) for the centralized controller, an approach that has been successfully demonstrated on flocking problems. MPC-based flocking controllers are high-performing but also computationally expensive. By learning a symmetric distributed neural flocking controller from a centralized MPC-based flocking controller, we achieve the best of both worlds: the neural controllers have high performance (on par with the MPC controllers) and high efficiency. Our experimental results demonstrate the sophisticated nature of the distributed controllers we learn. In particular, the neural controllers are capable of achieving myriad flocking-oriented control objectives, including flocking formation, collision avoidance, obstacle avoidance, predator avoidance, and target seeking. Moreover, they generalize the behavior seen in the training data in order to achieve these objectives in a significantly broader range of scenarios.