  Pursuit-evasion is the problem of capturing mobile targets with one or more
pursuers. We use deep reinforcement learning for pursuing an omni-directional
target with multiple, homogeneous agents that are subject to unicycle kinematic
constraints. We use shared experience to train a policy for a given number of
pursuers that is executed independently by each agent at run-time. The training
benefits from curriculum learning, a sweeping-angle ordering to locally
represent neighboring agents and encouraging good formations with reward
structure that combines individual and group rewards. Simulated experiments
with a reactive evader and up to eight pursuers show that our learning-based
approach, with non-holonomic agents, performs on par with classical algorithms
with omni-directional agents, and outperforms their non-holonomic adaptations.
The learned policy is successfully transferred to the real world in a
proof-of-concept demonstration with three motion-constrained pursuer drones.
