11 citations · 17 across the 8 of their papers we have counts for
9 papers
K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents
Hamed Khorasgani, Haiyan Wang, Hsiu-Khuern Tang +1
Traditionally, the performance of multi-agent deep reinforcement learning algorithms are demonstrated and validated in gaming environments where we often have a fixed number of age…
Data-driven Residual Generation for Early Fault Detection with Limited Data
Hamed Khorasgani, Ahmed Farahat, Chetan Gupta
Traditionally, fault detection and isolation community has used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems.…
An Offline Deep Reinforcement Learning for Maintenance Decision-Making
Hamed Khorasgani, Haiyan Wang, Chetan Gupta +1
Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years. Having access to…
Deep Reinforcement Learning with Adjustments
Hamed Khorasgani, Haiyan Wang, Chetan Gupta +1
Deep reinforcement learning (RL) algorithms can learn complex policies to optimize agent operation over time. RL algorithms have shown promising results in solving complicated prob…
A Non-linear Function-on-Function Model for Regression with Time Series Data
Qiyao Wang, Haiyan Wang, Chetan Gupta +2
In the last few decades, building regression models for non-scalar variables, including time series, text, image, and video, has attracted increasing interests of researchers from…
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
Hamed Khorasgani, Haiyan Wang, Chetan Gupta
Dynamic dispatching aims to smartly allocate the right resources to the right place at the right time. Dynamic dispatching is one of the core problems for operations optimization i…
Spatio-Temporal Functional Neural Networks
Aniruddha Rajendra Rao, Qiyao Wang, Haiyan Wang +2
Explosive growth in spatio-temporal data and its wide range of applications have attracted increasing interests of researchers in the statistical and machine learning fields. The s…
Dynamic Dispatching for Large-Scale Heterogeneous Fleet via Multi-agent Deep Reinforcement Learning
Chi Zhang, Philip Odonkor, Shuai Zheng +3
Dynamic dispatching is one of the core problems for operation optimization in traditional industries such as mining, as it is about how to smartly allocate the right resources to t…
Comparison of Model Predictive and Reinforcement Learning Methods for Fault Tolerant Control
Ibrahim Ahmed, Hamed Khorasgani, Gautam Biswas
A desirable property in fault-tolerant controllers is adaptability to system changes as they evolve during systems operations. An adaptive controller does not require optimal contr…