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About

Nessrine Khlif is a researcher advancing the field of autonomous mobile robotics, with a primary focus on reinforcement learning (RL) for path planning and navigation. Her work addresses a critical challenge in robotics: enabling mobile robots to navigate complex, dynamic environments without human intervention. Khlif’s most cited paper, "Reinforcement learning with modified exploration strategy for mobile robot path planning" (2023), has garnered 19 citations and introduces an innovative approach to improving RL exploration, a key bottleneck in training efficient navigation policies. She further demonstrates her expertise through a comparative analysis of modified Q-learning and Deep Q-Networks (DQN) for autonomous navigation (2024, 6 citations), providing valuable insights into algorithm selection for real-world applications. Khlif also contributed a comprehensive overview of RL for mobile robot navigation (2022) and a foundational study on kinematic controller design for trajectory tracking (2023). Her work bridges theoretical algorithm development with practical robotic implementation, offering clear benchmarks for convergence speed and success rates. With a growing citation impact, Khlif is establishing herself as a rising voice in the intersection of reinforcement learning and autonomous systems, making her research essential for students and engineers seeking to deploy intelligent, self-navigating robots.