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

## TL;DR

This paper introduces a reinforcement meta-learning based guidance law that uses only line of sight angle measurements for missile interception, eliminating the need for range estimation and adapting to target acceleration.

## Contribution

It presents a novel, passive seeker-compatible guidance policy optimized via reinforcement meta-learning, capable of real-time adaptation without range data.

## Key findings

- Performs as well as guidance with perfect target acceleration knowledge
- Does not require range estimation, suitable for passive seekers
- Computationally efficient and minimal memory requirement

## Abstract

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.