Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strike

TL;DR

This paper applies deep reinforcement learning to optimize weapons-to-target assignment in hypersonic strikes, achieving near-optimal results with significantly faster computation, enabling real-time autonomous decision-making in complex combat scenarios.

Contribution

The paper introduces a deep RL-based WTA policy for hypersonic strikes, demonstrating near-optimal performance and substantial speed improvements over traditional methods.

Findings

RL WTA policy achieves near-optimal target destruction

1000X faster computation enables real-time decision making

Robust performance across varied scenarios

Abstract

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.