Dynamic Pursuit-Evasion Scenarios With a Varying Number of Pursuers Using Deep Sets

The defence against unmanned aerial vehicles (UAVs) has become an essential topic in recent years. A possible solution that works as an effector against enemy UAVs employs a swarm of its own UAVs. Such a scenario can be modelled as a pursuit evasion scenario, which has been considered in the literature before. A possible solution uses a reinforcement learning approach in which a neural network steers the UAVs. However, previous approaches using multi layer perceptrons (MLPs) have an important caveat that their input dimension is fixed. This severely limits the flexibility of this approach, as changing the number of units in a swarm would require a model to be retrained. This paper presents a solution that employs a Deep Sets based model, allowing the user to change the number of agents inside a swarm as desired. It is shown that using Deep Sets is a viable method to solve a pursuit evasion scenario, in which the number of agents can vary between scenarios, but the trained model stays the same.