Dhulfiqar A. Alwahab

About

Dhulfiqar A. Alwahab is a researcher at the intersection of reinforcement learning and network systems, with a primary focus on intelligent network management. His most cited work, "On a Deep Q-Network-based Approach for Active Queue Management" (2021), has garnered 13 citations and represents a significant contribution to the field of network congestion control. In this paper, Alwahab leverages the powerful tools of deep Q-learning—a branch of reinforcement learning that has seen transformative growth over the past decade—to design a novel Active Queue Management (AQM) algorithm. This approach demonstrates how modern AI techniques, typically applied in robotics and automation, can be adapted to optimize network performance, offering a data-driven alternative to traditional heuristic-based AQM methods. By bridging the gap between reinforcement learning and network engineering, Alwahab’s work provides a practical pathway for more adaptive and efficient network traffic control. His research is particularly valuable for students and researchers exploring the application of deep reinforcement learning in real-world systems, showcasing how cutting-edge AI can solve longstanding challenges in network infrastructure.

Research Focus

Key Achievements

Top Papers

1. 1
   On a Deep Q-Network-based Approach for Active Queue Management

   13 citations · 2021

Key Collaborators

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