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

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