Model-Based Stochastic Search for Large Scale Optimization of Multi-Agent UAV Swarms

TL;DR

This paper introduces a model-based stochastic search approach, based on Evolution Strategies, for efficiently optimizing large-scale multi-agent UAV swarm behaviors in complex cooperative and competitive scenarios.

Contribution

It demonstrates that Evolution Strategies are a specific case of model-based stochastic search with proven convergence, applied to multi-agent UAV swarm optimization.

Findings

Effective in complex UAV swarm combat scenarios

Achieves scalable and efficient optimization

Demonstrates convergence properties of the method

Abstract

Recent work from the reinforcement learning community has shown that Evolution Strategies are a fast and scalable alternative to other reinforcement learning methods. In this paper we show that Evolution Strategies are a special case of model-based stochastic search methods. This class of algorithms has nice asymptotic convergence properties and known convergence rates. We show how these methods can be used to solve both cooperative and competitive multi-agent problems in an efficient manner. We demonstrate the effectiveness of this approach on two complex multi-agent UAV swarm combat scenarios: where a team of fixed wing aircraft must attack a well-defended base, and where two teams of agents go head to head to defeat each other.