Deep Reinforcement Learning for Weapons to Targets Assignment in a
  Hypersonic strike
Brian Gaudet Kris Drozd Roberto Furfaro
We use deep reinforcement learning (RL) to optimize a weapons to target
assignment (WTA) policy for multi-vehicle hypersonic strike against multiple
targets. The objective is to maximize the total value of destroyed targets in
each episode. Each randomly generated episode varies the number and initial
conditions of the hypersonic strike weapons (HSW) and targets, the value
distribution of the targets, and the probability of a HSW being intercepted. We
compare the performance of this WTA policy to that of a benchmark WTA policy
derived using non-linear integer programming (NLIP), and find that the RL WTA
policy gives near optimal performance with a 1000X speedup in computation time,
allowing real time operation that facilitates autonomous decision making in the
mission end game.