CUED Publications database

Hyper-parameter optimisation of Gaussian process reinforcement learning for statistical dialogue management

Chen, L and Su, PH and Gašić, M (2015) Hyper-parameter optimisation of Gaussian process reinforcement learning for statistical dialogue management. In: UNSPECIFIED pp. 407-411..

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Abstract

© 2015 Association for Computational Linguistics. Gaussian processes reinforcement learning provides an appealing framework for training the dialogue policy as it takes into account correlations of the objective function given different dialogue belief states, which can significantly speed up the learning. These correlations are modelled by the kernel function which may depend on hyper-parameters. So far, for real-world dialogue systems the hyperparameters have been hand-tuned, relying on the designer to adjust the correlations, or simple non-parametrised kernel functions have been used instead. Here, we examine different kernel structures and show that it is possible to optimise the hyperparameters from data yielding improved performance of the resulting dialogue policy. We confirm this in a real user trial.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:37
Last Modified: 22 Sep 2017 20:12
DOI: