Coordinating Team Tactics for Swarm-Versus-Swarm Adversarial Games

Although swarms of unmanned aerial vehicles have received much attention in the last few years, adversarial swarms (that is, competitive swarm-versus-swarm games) have been less well studied. In this paper, we demonstrate a deep reinforcement learning method to train a policy of fixed-wing aircraft agents to leverage hand-scripted tactics to exploit force concentration advantage and within-team coordination opportunities to destroy, or destroy, as many opponent team members as possible while preventing teammates from being attrited. The efficacy of agents using the policy network trained using the proposed method outperform teams utilizing only one of the handcrafted baseline tactics in N-vs-N engagements for N as small as two and as large as 64 as well as learner teams trained to vary their yaw rate actions, even when the trained team's agents' sensor range and teammate partnership possibility is constrained.