{"ID":2862647,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26002","arxiv_id":"2509.26002","title":"Towards Human Engagement with Realistic AI Combat Pilots","abstract":"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.","short_abstract":"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 traine...","url_abs":"https://arxiv.org/abs/2509.26002","url_pdf":"https://arxiv.org/pdf/2509.26002v1","authors":"[\"Ardian Selmonaj\",\"Giacomo Del Rio\",\"Adrian Schneider\",\"Alessandro Antonucci\"]","published":"2025-09-30T09:34:10Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.HC\",\"cs.LG\",\"cs.MA\",\"cs.RO\"]","methods":"[\"Reinforcement Learning\",\"LoRA\"]","has_code":false}