1.3k citations · 2.8k across the 15 of their papers we have counts for
7 papers · 1 filter
Deep Learning Methods for Credit Card Fraud Detection
Thanh Thi Nguyen, Hammad Tahir, Mohamed Abdelrazek +1
Credit card frauds are at an ever-increasing rate and have become a major problem in the financial sector. Because of these frauds, card users are hesitant in making purchases and…
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…
A Visual Communication Map for Multi-Agent Deep Reinforcement Learning
Ngoc Duy Nguyen, Thanh Thi Nguyen, Doug Creighton +1
Deep reinforcement learning has been applied successfully to solve various real-world problems and the number of its applications in the multi-agent settings has been increasing. M…
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications
Thanh Thi Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi
Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms however have faced great cha…
Multi-Agent Deep Reinforcement Learning with Human Strategies
Thanh Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi
Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning method…
A Human Mixed Strategy Approach to Deep Reinforcement Learning
Ngoc Duy Nguyen, Saeid Nahavandi, Thanh Nguyen
In 2015, Google's DeepMind announced an advancement in creating an autonomous agent based on deep reinforcement learning (DRL) that could beat a professional player in a series of…
A Multi-Objective Deep Reinforcement Learning Framework
Thanh Thi Nguyen, Ngoc Duy Nguyen, Peter Vamplew +3
This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supp…