# Towards Human Engagement with Realistic AI Combat Pilots

arXiv: https://arxiv.org/abs/2509.26002 · HTML: https://mlsanity.com/paper/2509.26002/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat](https://mlsanity.com/paper/2403.17533/) (2024)
- [Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2304.09669/) (2023)
- [Loyal Wingman Assessment: Social Navigation for Human-Autonomous Collaboration in Simulated Air Combat](https://mlsanity.com/paper/2405.00073/) (2024)

## Abstract (arXiv)

We present a system that enables real-time interaction between human users and agents trained to control fighter jets in simulated 3D air combat scenarios. The agents are trained in a dedicated environment using Multi-Agent Reinforcement Learning. A communication link is developed to allow seamless deployment of trained agents into VR-Forces, a widely used defense simulation tool for realistic tactical scenarios. This integration allows mixed simulations where human-controlled entities engage with intelligent agents exhibiting distinct combat behaviors. Our interaction model creates new opportunities for human-agent teaming, immersive training, and the exploration of innovative tactics in defense contexts.