Learning in POMDPs with Monte Carlo Tree Search
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
@article{arxiv.1806.05631,
title = {Learning in POMDPs with Monte Carlo Tree Search},
author = {Sammie Katt and Frans A. Oliehoek and Christopher Amato},
journal= {arXiv preprint arXiv:1806.05631},
year = {2018}
}