19 citations · 77 across the 16 of their papers we have counts for
16 papers · 1 filter
Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning
Takuya Kanazawa, Chetan Gupta
Sequential decision making in the real world often requires finding a good balance of conflicting objectives. In general, there exist a plethora of Pareto-optimal policies that emb…
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…
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…
Deep Time Series Models for Scarce Data
Qiyao Wang, Ahmed Farahat, Chetan Gupta +1
Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research. A comprehensive comparison of different time seri…
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…