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In the coming years, electric aircraft capable of vertical takeoff and landing (eVTOL) will bring about a revolutionary change in urban air mobility. However, this new paradigm demands developing specific tools to manage airspace and enable autonomous operations. Numerous manufacturers are currently working on the development of these aircraft. As adoption increases gradually, the expected high volume of simultaneous flights will present challenges for air traffic control systems. Furthermore, eVTOL aircraft have the potential to operate without onboard pilots, reducing costs but also increasing the complexity of safety solutions.

A redundant set of conflict detection and resolution systems is required to address these challenges. This study explores the potential of deep reinforcement learning models to solve this problem. By leveraging embedded systems such as ADS-B for independent conflict detection, deep reinforcement learning models can suggest actions even in scenarios where conflicts are unobserved. This flexibility makes them well-suited for resolving conflicts, as training a system with all possible conflict configurations is impractical.

The study employed a system based on Deep Q Network (DQN) models. These models adjusted aircraft trajectories by deviating minimally from their ideal paths to resolve conflicts. A customized simulator was also developed to test and compare multiple deep reinforcement learning agents with alternative strategies. The results demonstrate that these models can propose maneuvers that reduce conflict occurrences without significantly impacting aircraft displacement or fuel consumption.