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Sample-efficient batch reinforcement learning for dialogue management optimization

Sample-efficient batch reinforcement learning for dialogue management optimization Sample-Ef cient Batch Reinforcement Learning for Dialogue Management Optimization OLIVIER PIETQUIN, Sup lec and UMI 2958 (GeorgiaTech - CNRS) e MATTHIEU GEIST and SENTHILKUMAR CHANDRAMOHAN, Sup lec e HERVE FREZZA-BUET, Sup lec and UMI 2958 (GeorgiaTech - CNRS) e Spoken Dialogue Systems (SDS) are systems which have the ability to interact with human beings using natural language as the medium of interaction. A dialogue policy plays a crucial role in determining the functioning of the dialogue management module. Handcrafting the dialogue policy is not always an option, considering the complexity of the dialogue task and the stochastic behavior of users. In recent years approaches based on Reinforcement Learning (RL) for policy optimization in dialogue management have been proved to be an ef cient approach for dialogue policy optimization. Yet most of the conventional RL algorithms are data intensive and demand techniques such as user simulation. Doing so, additional modeling errors are likely to occur. This paper explores the possibility of using a set of approximate dynamic programming algorithms for policy optimization in SDS. Moreover, these algorithms are combined to a method for learning a sparse representation of the value function. Experimental results show that these algorithms when applied http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Speech and Language Processing (TSLP) Association for Computing Machinery

Sample-efficient batch reinforcement learning for dialogue management optimization

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISSN
1550-4875
DOI
10.1145/1966407.1966412
Publisher site
See Article on Publisher Site

Abstract

Sample-Ef cient Batch Reinforcement Learning for Dialogue Management Optimization OLIVIER PIETQUIN, Sup lec and UMI 2958 (GeorgiaTech - CNRS) e MATTHIEU GEIST and SENTHILKUMAR CHANDRAMOHAN, Sup lec e HERVE FREZZA-BUET, Sup lec and UMI 2958 (GeorgiaTech - CNRS) e Spoken Dialogue Systems (SDS) are systems which have the ability to interact with human beings using natural language as the medium of interaction. A dialogue policy plays a crucial role in determining the functioning of the dialogue management module. Handcrafting the dialogue policy is not always an option, considering the complexity of the dialogue task and the stochastic behavior of users. In recent years approaches based on Reinforcement Learning (RL) for policy optimization in dialogue management have been proved to be an ef cient approach for dialogue policy optimization. Yet most of the conventional RL algorithms are data intensive and demand techniques such as user simulation. Doing so, additional modeling errors are likely to occur. This paper explores the possibility of using a set of approximate dynamic programming algorithms for policy optimization in SDS. Moreover, these algorithms are combined to a method for learning a sparse representation of the value function. Experimental results show that these algorithms when applied

Journal

ACM Transactions on Speech and Language Processing (TSLP)Association for Computing Machinery

Published: May 1, 2011

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