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

arXiv: https://arxiv.org/abs/2310.18509 · HTML: https://mlsanity.com/paper/2310.18509/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Adaptive Approach Phase Guidance for a Hypersonic Glider via Reinforcement Meta Learning](https://mlsanity.com/paper/2107.14764/) (2021)
- [Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Learning](https://mlsanity.com/paper/2110.00634/) (2021)
- [Integrated and Adaptive Guidance and Control for Endoatmospheric Missiles via Reinforcement Learning](https://mlsanity.com/paper/2109.03880/) (2021)
- [Integrated Guidance and Control for Lunar Landing using a Stabilized Seeker](https://mlsanity.com/paper/2112.08540/) (2021)
- [Line of Sight Curvature for Missile Guidance using Reinforcement Meta-Learning](https://mlsanity.com/paper/2205.00085/) (2022)

## Built on by (most cited first, 1 of 1)

- [Safety-Critical Reinforcement Learning with Viability-Based Action Shielding for Hypersonic Longitudinal Flight](https://mlsanity.com/paper/2602.03968/) (2026)

## Abstract (arXiv)

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.