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Found 5 papers, 0 papers with code
On-Robot Bayesian Reinforcement Learning for POMDPs
Bayesian reinforcement learning (BRL), thanks to its sample efficiency and ability to exploit prior knowledge, is uniquely positioned as such a solution method.
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs
Under this parameterization, in contrast to previous work, the belief over the state and dynamics is a more scalable inference problem.
Removing Dynamic Objects for Static Scene Reconstruction using Light Fields
In this paper we present a method to synthesize a refocused image of the static background in the presence of dynamic objects that uses a light-field acquired with a linear camera array.
Bayesian Reinforcement Learning in Factored POMDPs
Bayesian approaches provide a principled solution to the exploration-exploitation trade-off in Reinforcement Learning.
Learning in POMDPs with Monte Carlo Tree Search
The POMDP is a powerful framework for reasoning under outcome and information uncertainty, but constructing an accurate POMDP model is difficult.
