203 citations · 262 across the 22 of their papers we have counts for
5 papers · 1 filter
Rating-based Reinforcement Learning
Devin White, Mingkang Wu, Ellen Novoseller +3
This paper develops a novel rating-based reinforcement learning approach that uses human ratings to obtain human guidance in reinforcement learning. Different from the existing pre…
Fairness in Preference-based Reinforcement Learning
Umer Siddique, Abhinav Sinha, Yongcan Cao
In this paper, we address the issue of fairness in preference-based reinforcement learning (PbRL) in the presence of multiple objectives. The main objective is to design control po…
Learn to Exceed: Stereo Inverse Reinforcement Learning with Concurrent Policy Optimization
Feng Tao, Yongcan Cao
In this paper, we study the problem of obtaining a control policy that can mimic and then outperform expert demonstrations in Markov decision processes where the reward function is…
Graph Based Multi-layer K-means++ (G-MLKM) for Sensory Pattern Analysis in Constrained Spaces
Feng Tao, Rengan Suresh, Johnathan Votion +1
In this paper, we focus on developing a novel unsupervised machine learning algorithm, named graph based multi-layer k-means++ (G-MLKM), to solve data-target association problem wh…
Deep Model Compression Via Two-Stage Deep Reinforcement Learning
Huixin Zhan, Wei-Ming Lin, Yongcan Cao
Besides accuracy, the model size of convolutional neural networks (CNN) models is another important factor considering limited hardware resources in practical applications. For exa…