Papers
1
Total Citations
4
H-Index
1
About
MW Fakhr is a researcher focused on the intersection of reinforcement learning and autonomous systems, with a particular emphasis on path planning for self-driving vehicles. Their most-cited work, "Autonomous Vehicle Path Planning using Q-Learning" (2021), has garnered 4 citations and explores how Q-learning algorithms can be adapted from discrete applications to the complex, continuous environments of autonomous navigation. This contribution addresses a critical challenge in robotics and driverless technology—enabling vehicles to make real-time, optimal routing decisions without human intervention. Fakhr’s research bridges theoretical machine learning and practical engineering, offering insights into how reinforcement learning can enhance the safety and efficiency of autonomous systems. While their citation count is modest, the work represents a foundational step in applying Q-learning to real-world vehicular path planning, a rapidly evolving field with significant implications for transportation and logistics. Fakhr’s dedication to solving core navigation problems positions them as an emerging voice in autonomous vehicle research, with potential for broader impact as the technology matures.
Research Focus
Key Achievements
Top Papers
- 1Autonomous Vehicle Path Planning using Q-Learning4 citations · 2021