13 citations · 41 across the 15 of their papers we have counts for
15 papers
On-Robot Bayesian Reinforcement Learning for POMDPs
Hai Nguyen, Sammie Katt, Yuchen Xiao +1
Robot learning is often difficult due to the expense of gathering data. The need for large amounts of data can, and should, be tackled with effective algorithms and leveraging expe…
Learning from Pixels with Expert Observations
Minh-Huy Hoang, Long Dinh, Hai Nguyen
In reinforcement learning (RL), sparse rewards can present a significant challenge. Fortunately, expert actions can be utilized to overcome this issue. However, acquiring explicit…
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…
A Model-Constrained Tangent Slope Learning Approach for Dynamical Systems
Hai V. Nguyen, Tan Bui-Thanh
Real-time accurate solutions of large-scale complex dynamical systems are in critical need for control, optimization, uncertainty quantification, and decision-making in practical e…
Hierarchical Reinforcement Learning under Mixed Observability
Hai Nguyen, Zhihan Yang, Andrea Baisero +3
The framework of mixed observable Markov decision processes (MOMDP) models many robotic domains in which some state variables are fully observable while others are not. In this wor…
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…
TNet: A Model-Constrained Tikhonov Network Approach for Inverse Problems
Hai V. Nguyen, Tan Bui-Thanh
Deep Learning (DL), in particular deep neural networks (DNN), by default is purely data-driven and in general does not require physics. This is the strength of DL but also one of i…
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…
Review, Analysis and Design of a Comprehensive Deep Reinforcement Learning Framework
Ngoc Duy Nguyen, Thanh Thi Nguyen, Hai Nguyen +2
The integration of deep learning to reinforcement learning (RL) has enabled RL to perform efficiently in high-dimensional environments. Deep RL methods have been applied to solve m…
Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization
Adarsh Sehgal, Hung Manh La, Sushil J. Louis +1
Reinforcement learning (RL) enables agents to take decision based on a reward function. However, in the process of learning, the choice of values for learning algorithm parameters…
Deep Learning with Experience Ranking Convolutional Neural Network for Robot Manipulator
Hai Nguyen, Hung Manh La, Matthew Deans
Supervised learning, more specifically Convolutional Neural Networks (CNN), has surpassed human ability in some visual recognition tasks such as detection of traffic signs, faces a…
Semi-supervised learning of hierarchical representations of molecules using neural message passing
Hai Nguyen, Shin-ichi Maeda, Kenta Oono
With the rapid increase of compound databases available in medicinal and material science, there is a growing need for learning representations of molecules in a semi-supervised ma…