317 citations · 397 across the 11 of their papers we have counts for
6 papers · 1 filter
Contrastive Variational Reinforcement Learning for Complex Observations
Xiao Ma, Siwei Chen, David Hsu +1
Deep reinforcement learning (DRL) has achieved significant success in various robot tasks: manipulation, navigation, etc. However, complex visual observations in natural environmen…
DinerDash Gym: A Benchmark for Policy Learning in High-Dimensional Action Space
Siwei Chen, Xiao Ma, David Hsu
It has been arduous to assess the progress of a policy learning algorithm in the domain of hierarchical task with high dimensional action space due to the lack of a commonly accept…
Balanced Meta-Softmax for Long-Tailed Visual Recognition
Jiawei Ren, Cunjun Yu, Shunan Sheng +4
Deep classifiers have achieved great success in visual recognition. However, real-world data is long-tailed by nature, leading to the mismatch between training and testing distribu…
Discriminative Particle Filter Reinforcement Learning for Complex Partial Observations
Xiao Ma, Peter Karkus, David Hsu +2
Deep reinforcement learning is successful in decision making for sophisticated games, such as Atari, Go, etc. However, real-world decision making often requires reasoning with part…
Differentiable Algorithm Networks for Composable Robot Learning
Peter Karkus, Xiao Ma, David Hsu +3
This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a…
Particle Filter Recurrent Neural Networks
Xiao Ma, Peter Karkus, David Hsu +1
Recurrent neural networks (RNNs) have been extraordinarily successful for prediction with sequential data. To tackle highly variable and noisy real-world data, we introduce Particl…