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Márk Mitrenga

Developer
Email
@email
Phone
+3612796134
Address
H-1111 Budapest, Kende u. 13-17.
Room number
K223

Systems and Control Lab

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Introduction

Márk Mitrenga is a BSc student in Vehicle Engineering at the Budapest University of Technology and Economics, specializing in Vehicle Mechatronics, and is scheduled to complete his final examination in January 2026. Following this, he will continue his studies in the Autonomous Vehicle Control Engineering MSc program at BME.
His research and development interests primarily focus on applications of artificial intelligence, with particular emphasis on computer vision, deep learning, and (multimodal) large language models. In his current work, he addresses perception and decision-support tasks for autonomous vehicles as well as humanoid and quadruped robotic systems, where the processing of visual information and its integration with control systems play a key role. His goal is to work as a developer and researcher at HUN-REN SZTAKI on state-of-the-art AI-based solutions that contribute to the development of intelligent, adaptive, and reliable autonomous systems.

Achievements

  • Award: Application of Monte Carlo Tree Search in Reinforcement Learning for Lane Keeping (TDK 2023)
  • 3rd Place: Lane-Independent Highway Traffic Management for
    Random Anomalies Using Reinforcement Learning (TDK 2024)
  • 2rd Place: Lane-Independent Highway Traffic Management for
    Random Anomalies Using Reinforcement Learning (OTDK 2025)
  • Award: Examining the visual capabilities of multimodal
    large language models for automotive applications (TDK 2025)
  • Award: Latent space based redundancy reduction for improved vehicle model
    identification (TDK 2025)

Publications (MTMT)

  • I. G. Knáb, M. Mitrenga and B. Kővári, "Diversity: A Key Component in Homogeneous Multi-Agent Reinforcement Learning," 2025 International Conference on Control, Automation and Diagnosis (ICCAD), Barcelona, Spain, 2025, pp. 1-6, doi: 10.1109/ICCAD64771.2025.11099398. keywords: {Training;Energy consumption;Adaptive systems;System performance;Transportation;Training data;Reinforcement learning;Computer architecture;Traffic control;Topology;Artificial Intelligence;Reinforcement Learning;Training Efficiency;Traffic Control}

Languages

  • English (B2)
  • German (B1)