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stat.ML2018
Variational Inference for Data-Efficient Model Learning in POMDPs
Sebastian Tschiatschek, Kai Arulkumaran, Jan Stühmer +1
Partially observable Markov decision processes (POMDPs) are a powerful abstraction for tasks that require decision making under uncertainty, and capture a wide range of real world…
stat.ML2018
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth
Learning from small data sets is critical in many practical applications where data collection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Me…