Scalable Swarm Control Using Deep Reinforcement Learning

Autonomous swarm navigation has been extensively studied due to its wide range of applications, from agriculture to surveillance and defense. Among the techniques used for swarm coordination, multi-agent reinforcement learning (MARL) has shown promise but is hindered by two main challenges. The first is the stochasticity of the environment, which is caused by the dynamic interaction among agents. The second is scalability, which becomes an issue as larger swarms require more complex neural networks and computational resources. To address these challenges, we propose a pipeline in which agents are trained using deep reinforcement learning in a single-agent, static environment. The resulting policy is then applied to multi-agent scenarios. We present a framework detailing this approach and its components. Our results show that a policy trained in a single-agent, static setting can be generalized effectively to multi-agent environments, mitigating the stochasticity issue. Furthermore, our model achieved collective behavior relying only on local information (agents in its neighborhood and a shared common goal), enabling scalability to large swarms. We compared our approach with a classical swarm control algorithm (flocking control) and a MARL approach (MADDPG), highlighting its efficiency and scalability. Our framework's performance is comparable with these two baselines, even performing better in some cases.