Learning in POMDPs with Monte Carlo Tree Search

Sammie Katt, Frans A. Oliehoek, Christopher Amato
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1819-1827, 2017.

Abstract

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. BA-POMDPs are a Bayesian RL approach that, in principle, allows for an optimal trade-off between exploitation and exploration. Unfortunately, BA-POMDPs are currently impractical to solve for any non-trivial domain. In this paper, we extend the Monte-Carlo Tree Search method POMCP to BA-POMDPs and show that the resulting method, which we call BA-POMCP, is able to tackle problems that previous solution methods have been unable to solve. Additionally, we introduce several techniques that exploit the BA-POMDP structure to improve the efficiency of BA-POMCP along with proof of their convergence.

Cite this Paper

BibTeX
@InProceedings{pmlr-v70-katt17a, title = {Learning in {POMDP}s with {M}onte {C}arlo Tree Search}, author = {Sammie Katt and Frans A. Oliehoek and Christopher Amato}, booktitle = {Proceedings of the 34th International Conference on Machine Learning}, pages = {1819--1827}, year = {2017}, editor = {Precup, Doina and Teh, Yee Whye}, volume = {70}, series = {Proceedings of Machine Learning Research}, month = {06--11 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v70/katt17a/katt17a.pdf}, url = {https://proceedings.mlr.press/v70/katt17a.html}, abstract = {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. BA-POMDPs are a Bayesian RL approach that, in principle, allows for an optimal trade-off between exploitation and exploration. Unfortunately, BA-POMDPs are currently impractical to solve for any non-trivial domain. In this paper, we extend the Monte-Carlo Tree Search method POMCP to BA-POMDPs and show that the resulting method, which we call BA-POMCP, is able to tackle problems that previous solution methods have been unable to solve. Additionally, we introduce several techniques that exploit the BA-POMDP structure to improve the efficiency of BA-POMCP along with proof of their convergence.} }
Endnote
%0 Conference Paper %T Learning in POMDPs with Monte Carlo Tree Search %A Sammie Katt %A Frans A. Oliehoek %A Christopher Amato %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-katt17a %I PMLR %P 1819--1827 %U https://proceedings.mlr.press/v70/katt17a.html %V 70 %X 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. BA-POMDPs are a Bayesian RL approach that, in principle, allows for an optimal trade-off between exploitation and exploration. Unfortunately, BA-POMDPs are currently impractical to solve for any non-trivial domain. In this paper, we extend the Monte-Carlo Tree Search method POMCP to BA-POMDPs and show that the resulting method, which we call BA-POMCP, is able to tackle problems that previous solution methods have been unable to solve. Additionally, we introduce several techniques that exploit the BA-POMDP structure to improve the efficiency of BA-POMCP along with proof of their convergence.
APA
Katt, S., Oliehoek, F.A. & Amato, C.. (2017). Learning in POMDPs with Monte Carlo Tree Search. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:1819-1827 Available from https://proceedings.mlr.press/v70/katt17a.html.

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