13 citations · 27 across the 7 of their papers we have counts for
10 papers
Leveraging Fully Observable Policies for Learning under Partial Observability
Hai Nguyen, Andrea Baisero, Dian Wang +2
Reinforcement learning in partially observable domains is challenging due to the lack of observable state information. Thankfully, learning offline in a simulator with such state i…
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs
Sammie Katt, Hai Nguyen, Frans A. Oliehoek +1
While reinforcement learning (RL) has made great advances in scalability, exploration and partial observability are still active research topics. In contrast, Bayesian RL (BRL) pro…
Recurrent Off-policy Baselines for Memory-based Continuous Control
Zhihan Yang, Hai Nguyen
When the environment is partially observable (PO), a deep reinforcement learning (RL) agent must learn a suitable temporal representation of the entire history in addition to a str…
Multi-directional Bicycle Robot for Steel Structure Inspection
Son Thanh Nguyen, Hai Nguyen, Son Tien Bui +2
This paper presents a novel design of a multi-directional bicycle robot, which targets inspecting general ferromagnetic structures including complex-shaped structures. The locomoti…
Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability
Hai Nguyen, Brett Daley, Xinchao Song +2
Many important robotics problems are partially observable in the sense that a single visual or force-feedback measurement is insufficient to reconstruct the state. Standard approac…
A Deep Learning-Based Autonomous RobotManipulator for Sorting Application
Hoang-Dung Bui, Hai Nguyen, Hung Manh La +1
Robot manipulation and grasping mechanisms have received considerable attention in the recent past, leading to the development of wide range of industrial applications. This paper…