  Development of guidance, navigation and control frameworks/algorithms for
swarms attracted significant attention in recent years. That being said,
algorithms for planning swarm allocations/trajectories for engaging with enemy
swarms is largely an understudied problem. Although small-scale scenarios can
be addressed with tools from differential game theory, existing approaches fail
to scale for large-scale multi-agent pursuit evasion (PE) scenarios. In this
work, we propose a reinforcement learning (RL) based framework to decompose to
large-scale swarm engagement problems into a number of independent multi-agent
pursuit-evasion games. We simulate a variety of multi-agent PE scenarios, where
finite time capture is guaranteed under certain conditions. The calculated PE
statistics are provided as a reward signal to the high level allocation layer,
which uses an RL algorithm to allocate controlled swarm units to eliminate
enemy swarm units with maximum efficiency. We verify our approach in
large-scale swarm-to-swarm engagement simulations.
