Mr. Ammar Khaleel l Reinforcement Learning | Research Excellence Award
Széchenyi István Egyetem | Hungary
Mr. Ammar Khaleel PhD-level researcher in computer science with ongoing doctoral training, focusing on reinforcement learning–based decision making for autonomous vehicles. Experience includes designing, training, and evaluating deep reinforcement learning and control algorithms for autonomous driving, particularly lane-changing, within large-scale traffic simulations using SUMO and the TraCI Python API. Research interests span reinforcement learning, deep learning, model predictive control, intelligent transportation systems, and traffic modeling. Technical expertise covers Python, C/C++, simulation frameworks, and reproducible research workflows. Academic contributions emphasize simulation-driven experimentation and algorithmic innovation; no formal awards are listed. Overall, the work aims to advance safe, efficient, and intelligent mobility systems.
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Featured Publications
Exploration Techniques in Reinforcement Learning for Autonomous Vehicles
– Engineering Proceedings, 2024
A Multi-Levels RNG Permutation
– Indonesian Journal of Electrical Engineering and Computer Science, 2019
A New Permutation Method for Sequence of Order 28
– Journal of Theoretical and Applied Information Technology, 2019
N/A and Signature Analysis for Malwares Detection and Removal
– Indian Journal of Science and Technology, 2019