6 citations · 9 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 6 cited
Interpretable Model-based Hierarchical Reinforcement Learning using Inductive Logic Programming
Duo Xu, Faramarz Fekri
Recently deep reinforcement learning has achieved tremendous success in wide ranges of applications. However, it notoriously lacks data-efficiency and interpretability. Data-effici…
cs.NE2020
Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback
Duo Xu, Mohit Agarwal, Ekansh Gupta +2
Providing Reinforcement Learning (RL) agents with human feedback can dramatically improve various aspects of learning. However, previous methods require human observer to give inpu…
cs.LG2019★ 3 cited
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler
Duo Xu
State space models (SSM) have been widely applied for the analysis and visualization of large sequential datasets. Sequential Monte Carlo (SMC) is a very popular particle-based met…