1 citations · 1 across the 1 of their papers we have counts for
4 papers
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs
Sammie Katt, Hai Nguyen, Frans A. Oliehoek +1
While reinforcement learning (RL) has made great advances in scalability, exploration and partial observability are still active research topics. In contrast, Bayesian RL (BRL) pro…
Removing Dynamic Objects for Static Scene Reconstruction using Light Fields
Pushyami Kaveti, Sammie Katt, Hanumant Singh
There is a general expectation that robots should operate in environments that consist of static and dynamic entities including people, furniture and automobiles. These dynamic env…
Bayesian Reinforcement Learning in Factored POMDPs
Sammie Katt, Frans Oliehoek, Christopher Amato
Bayesian approaches provide a principled solution to the exploration-exploitation trade-off in Reinforcement Learning. Typical approaches, however, either assume a fully observable…
Learning in POMDPs with Monte Carlo Tree Search
Sammie Katt, Frans A. Oliehoek, Christopher Amato
The POMDP is a powerful framework for reasoning under outcome and information uncertainty, but constructing an accurate POMDP model is difficult. Bayes-Adaptive Partially Observabl…