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Worldwide, many companies are working towards safe and innovative control systems for Autonomous Vehicles (AVs). A key component is Autonomous Intersection Management (AIM) systems, which operate at the level of traffic intersections and manage the right-of-way for AVs, thereby improving flow and safety. AIM traditionally uses control policies based on simple rules. However, Deep Reinforcement Learning (DRL) can provide advanced control policies with the advantage of proactively reacting and forecasting hazardous situations. The main drawback of DRL is the training time, which is fast in simple tasks but not negligible when addressing real-world problems with multiple agents. Learning from Demonstrations (LfD) emerged to solve this problem, significantly speeding up training, and reducing the exploration problem. The challenge is that LfD requires an expert to extract new demonstrations. Therefore …