Evaluation of air combat simulation for multiagent reinforcement learning approaches

<jats:p>Reinforcement Learning (RL) is increasingly used to develop intelligent control policies for complex and dynamic systems such as air combat simulations. This report reviews several open-source frameworks, including JSBSim, FlightGear, CyberBattleSim, CloseAirCombat, and DogFight2, with a focus on their suitability for RL-based aircraft simulation and multiagent research. The evaluation considers each platform’s flexibility, ease of integration, and support for various RL methods. Based on this analysis, the report proposes a modular simulation framework that combines accurate flight dynamics with standardized RL interfacing. This work aims to accelerate research on autonomous air combat agents and provide a foundation for future advancements in multiagent RL.</jats:p>