Learning Environment for the Air Domain (LEAD)

ABSTRACT A substantial part of fighter pilot training is simulation-based and involves computer-generated forces controlled by predefined behavior models. The behavior models are typically manually created by eliciting knowledge from experienced pilots, which is a time-consuming process. Despite the work put in, the behavior models are often unsatisfactory due to their predictable nature and lack of adaptivity, forcing instructors to spend time manually monitoring and controlling them. Reinforcement and imitation learning pose as alternatives to handcrafted models. This paper presents the Learning Environment for the Air Domain (LEAD), a system for creating and integrating intelligent air combat behavior in military simulations. By incorporating the popular programming library and interface Gymnasium, LEAD allows users to apply readily available machine learning algorithms. Additionally, LEAD can communicate with third-party simulation software through distributed simulation protocols, which allows behavior models to be learned and employed using simulation systems of different fidelities. 1 INTRODUCTION A large part of the training fighter pilots undergo occurs in simulators under instructor supervision. In these simulators, the pilots practice tactics and operations by engaging in scenarios including friendly and hostile forces, often represented by computer-generated forces (CGFs), which are autonomous or semi-autonomous actors used in military simulation (Løvlid et al. 2017). These CGFs must behave in a way that accelerates training and builds the necessary competence of the pilots. Still, a current limitation to using CGFs for training is that their behaviors often come across as predictable, inviting pilots to exploit their vulnerabilities rather than focus on achieving the training objectives (Toubman 2020, ch. 1). Such constraints in the behavior models force instructors to micromanage the CGFs, restricting the complexity of scenarios that can be managed and trained (Källström et al. 2022). Besides, qualified instructors are both in short supply and on tight schedules, meaning they should devote their full attention to giving instructions and feedback to pilots. Modeling adaptive and intelligent air combat behavior for CGFs is thus a key challenge.