Katt, Sammie, Oliehoek, Frans A ORCID: 0000-0003-4372-5055 and Amato, Christopher
(2017)
Learning in POMDPs with Monte Carlo Tree Search.
, 34th International Conference on Machine Learning (ICML 2017).
Text
Katt17ICML.pdf - Author Accepted Manuscript Access to this file is embargoed until Unspecified. Download (489kB) |
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.
Item Type: | Conference or Workshop Item (Unspecified) |
---|---|
Uncontrolled Keywords: | cs.AI, cs.AI, cs.LG |
Depositing User: | Symplectic Admin |
Date Deposited: | 13 Jun 2017 07:52 |
Last Modified: | 19 Jan 2023 07:03 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3007954 |