23 citations · 24 across the 3 of their papers we have counts for
5 papers
State-Aware Variational Thompson Sampling for Deep Q-Networks
Siddharth Aravindan, Wee Sun Lee
Thompson sampling is a well-known approach for balancing exploration and exploitation in reinforcement learning. It requires the posterior distribution of value-action functions to…
Neuralizing Efficient Higher-order Belief Propagation
Mohammed Haroon Dupty, Wee Sun Lee
Graph neural network models have been extensively used to learn node representations for graph structured data in an end-to-end setting. These models often rely on localized first…
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…
Multiplicative Gaussian Particle Filter
Xuan Su, Wee Sun Lee, Zhen Zhang
We propose a new sampling-based approach for approximate inference in filtering problems. Instead of approximating conditional distributions with a finite set of states, as done in…
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…