Hoang-huu

Papers

Total Citations

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About

Hoang-huu is a researcher focused on advancing path planning and reinforcement learning for mobile robotics. Their key contributions center on improving the efficiency of model-based reinforcement learning algorithms, particularly through their work on the extended Dyna-Q algorithm. In their most-cited paper, "Extended Dyna-Q Algorithm for Path Planning of Mobile Robots" (2011, 10 citations), they address a critical weakness in the standard Dyna-Q approach—the agent's blind exploration in early episodes. By introducing a maximum likelihood model of all state-action pairs, Hoang-huu's method enables more informed decision-making, reducing wasteful random trials and accelerating convergence to optimal paths. This innovation has practical implications for autonomous navigation in unknown environments, where efficiency and adaptability are paramount. While their citation count reflects a focused but impactful contribution, the work stands as a thoughtful refinement of a classic algorithm, offering a pragmatic solution to a real-world robotics challenge. For students and researchers in robotics and AI, Hoang-huu's research exemplifies how targeted algorithmic improvements can yield meaningful gains in autonomous system performance.