Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers

Abstract

We show how a symmetric and fully distributed flocking controller can be synthesized using Deep Learning from a centralized flocking controller. Our approach is based on Supervised Learning, with the centralized controller providing the training data, in the form of trajectories of state-action pairs. We use Model Predictive Control (MPC) for the centralized controller, an approach that we have successfully demonstrated on flocking problems. MPC-based flocking controllers are high-performing but also computationally expensive. By learning a symmetric and distributed neural flocking controller from a centralized MPC-based one, 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 to achieve these objectives in a significantly broader range of scenarios. In terms of verification of our neural flocking controller, we use a form of statistical model checking to compute confidence intervals for its convergence rate and time to convergence.

Consensus, cooperative learning, and flocking for multiagent predator avoidance

“…39,40 There are also cases in which reinforcement learning is implemented to teach agents to go to specific targets, but in cases where learning fails, a flocking algorithm to avoid obstacles is used. 41 Flocking by itself is not particularly useful if the agents flock directly to a predator so this work aims to combine learning with flocking to effectively escape from predators. Other flocking implementations such as that proposed in the literature 42 allow agents to flock together with minimal information transfer, but the formation is not ideal for quickly avoiding a predator.…”