  Unmanned Aerial Vehicles (UAVs), autonomously-guided aircraft, are widely
used for tasks involving surveillance and reconnaissance. A version of the
pursuit-evasion problems centered around UAVs and its variants has been
extensively studied in recent years due to numerous breakthroughs in AI. We
present an approach to UAV pursuit-evasion in a 2D aerial-engagement
environment using reinforcement learning (RL), a machine learning paradigm
concerned with goal-oriented algorithms. In this work, a UAV wielding the
greedy shooter strategy engages with a UAV trained using deep Q-learning
techniques. Simulated results show that the latter UAV wins every engagement in
which the UAVs are suffciently separated during initialization. This approach
highlights an exhaustive and robust application of reinforcement learning to
pursuit-evasion that provides insight into effective strategies for UAV flight
and interaction.
