Ammar Khaleel is a PhD student in Computer Science at Széchenyi István Egyetem, specializing in intelligent and autonomous transportation systems. His research focuses on reinforcement learning–based decision making for autonomous vehicles, with particular emphasis on autonomous driving and lane-changing strategies. He has strong expertise in deep reinforcement learning methods such as DDPG and PPO, as well as Model Predictive Control. Ammar actively works with traffic simulation environments using SUMO and the TraCI Python API to model and evaluate complex traffic scenarios. His technical skills include Python and C/C++, along with experience in Git, LaTeX, and scientific software tools.
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