Tung-Long Vuong

About

Tung-Long Vuong is a researcher in artificial intelligence, with a focus on deep reinforcement learning and its applications in robotics and autonomous navigation. His work explores how neural networks can be integrated with reinforcement learning to enable intelligent agents to perceive, remember, and act within complex environments. Vuong’s notable contribution, "Vision Memory for Target Object Navigation Using Deep Reinforcement Learning: An Empirical Study," investigates how memory-augmented deep learning models can improve an agent’s ability to locate and navigate toward specific objects using visual input alone. This study, which has garnered 2 citations, provides empirical insights into the challenges of combining high-dimensional visual data with experience-driven learning—a critical step toward more capable autonomous systems. By addressing the intersection of computer vision and decision-making, Vuong’s research helps lay the groundwork for advanced robotic applications, from household assistants to search-and-rescue drones. His work is particularly relevant for students and researchers interested in how deep neural networks can be trained to not only perceive the world but also remember and act upon it, bridging the gap between raw sensory data and purposeful behavior.

Research Focus

Key Achievements

Top Papers

1. 1
   Vision Memory for Target Object Navigation Using Deep Reinforcement Learning: An Empirical Study

   2 citations · 2018

Key Collaborators

Contact & Links