# Reinforcement Learning for Angle-Only Intercept Guidance of Maneuvering Targets

arXiv: https://arxiv.org/abs/1906.02113 · HTML: https://mlsanity.com/paper/1906.02113/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning](https://mlsanity.com/paper/2606.13605/) (2026)
- [Guidance Design for Escape Flight Vehicle Using Evolution Strategy Enhanced Deep Reinforcement Learning](https://mlsanity.com/paper/2405.03711/) (2024)

## Abstract (arXiv)

We present a novel guidance law that uses observations consisting solely of seeker line of sight angle measurements and their rate of change. The policy is optimized using reinforcement meta-learning and demonstrated in a simulated terminal phase of a mid-course exo-atmospheric interception. Importantly, the guidance law does not require range estimation, making it particularly suitable for passive seekers. The optimized policy maps stabilized seeker line of sight angles and their rate of change directly to commanded thrust for the missile's divert thrusters. The use of reinforcement meta-learning allows the optimized policy to adapt to target acceleration, and we demonstrate that the policy performs as well as augmented zero-effort miss guidance with perfect target acceleration knowledge. The optimized policy is computationally efficient and requires minimal memory, and should be compatible with today's flight processors.