Search Results for author: Hai Nguyen

Found 8 papers, 5 papers with code

BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs

no code implementations17 Feb 2022 Sammie Katt, Hai Nguyen, Frans A. Oliehoek, Christopher Amato

Under this parameterization, in contrast to previous work, the belief over the state and dynamics is a more scalable inference problem.

Recurrent Off-policy Baselines for Memory-based Continuous Control

1 code implementation25 Oct 2021 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 strategy to control.

Continuous Control

Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability

1 code implementation19 Oct 2020 Hai Nguyen, Brett Daley, Xinchao Song, Christopher Amato, Robert Platt

Many important robotics problems are partially observable in the sense that a single visual or force-feedback measurement is insufficient to reconstruct the state.

Review, Analysis and Design of a Comprehensive Deep Reinforcement Learning Framework

no code implementations27 Feb 2020 Ngoc Duy Nguyen, Thanh Thi Nguyen, Hai Nguyen, Doug Creighton, Saeid Nahavandi

However, development of a deep RL-based system is challenging because of various issues such as the selection of a suitable deep RL algorithm, its network configuration, training time, training methods, and so on.


Deep Learning with Experience Ranking Convolutional Neural Network for Robot Manipulator

1 code implementation16 Sep 2018 Hai Nguyen, Hung Manh La, Matthew Deans

One of the reasons that human are better learners in these tasks is that we are embedded with much prior knowledge of the world.


Semi-supervised learning of hierarchical representations of molecules using neural message passing

1 code implementation28 Nov 2017 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 manner.

Cannot find the paper you are looking for? You can Submit a new open access paper.