Evolving cost functions for model predictive control of multi-agent UAV combat swarms

Abstract

Recent advances in sampling-based Model Predictive Control (MPC) methods have enabled the control of nonlinear stochastic dynamical systems with complex and non-smooth cost functions. However, the main drawback of these methods is that they can be myopic with respect to high-level tasks, since MPC relies on predicting dynamics within a short time horizon. Furthermore, designing cost functions which capture high-level information may be prohibitive for complex tasks, especially multi-agent scenarios. Here we propose a hierarchical approach to this problem where the NeuroEvolution of Augmenting Topologies (NEAT) algorithm is used to build cost functions for an MPC trajectory optimization algorithm known as Model-Predictive Path Integral (MPPI) control. MPPI and NEAT are particularly well-suited to one another since MPPI can control an agent in a way that minimizes a non-differentiable cost function (including logic or non-smooth functions), while NEAT can build a neural network comprised of any arbitrary activation functions, including those which are non-differentiable or logic-based. We utilize this approach in controlling agile swarms of unmanned aerial vehicles (UAVs) in a simulated swarm vs. swarm combat scenario.