Learning in POMDPs with Monte Carlo Tree Search

ICML 2017 Sammie KattFrans A. OliehoekChristopher 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 Observable Markov Decision Processes (BA-POMDPs) extend POMDPs to allow the model to be learned during execution... (read more)

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