# Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2304.09669 · HTML: https://mlsanity.com/paper/2304.09669/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [Engagement Decision Support for Beyond Visual Range Air Combat](https://mlsanity.com/paper/2111.03059/) (2021)
- [Supervised Machine Learning for Effective Missile Launch Based on Beyond Visual Range Air Combat Simulations](https://mlsanity.com/paper/2207.04188/) (2022)
- [ASA: A Simulation Environment for Evaluating Military Operational Scenarios](https://mlsanity.com/paper/2207.12084/) (2022)
- [Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network](https://mlsanity.com/paper/2111.04474/) (2021)

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

- [NavigScene: Bridging Local Perception and Global Navigation for Beyond-Visual-Range Autonomous Driving](https://mlsanity.com/paper/2507.05227/) (2025)
- [Towards Human Engagement with Realistic AI Combat Pilots](https://mlsanity.com/paper/2509.26002/) (2025)

## Abstract (arXiv)

This work contributes to developing an agent based on deep reinforcement learning capable of acting in a beyond visual range (BVR) air combat simulation environment. The paper presents an overview of building an agent representing a high-performance fighter aircraft that can learn and improve its role in BVR combat over time based on rewards calculated using operational metrics. Also, through self-play experiments, it expects to generate new air combat tactics never seen before. Finally, we hope to examine a real pilot's ability, using virtual simulation, to interact in the same environment with the trained agent and compare their performances. This research will contribute to the air combat training context by developing agents that can interact with real pilots to improve their performances in air defense missions.