@Article{Supelec808,
author = {Lucie Daubigney and Matthieu Geist and Senthilkumar Chandramohan and Olivier Pietquin},
title = {A Comprehensive Reinforcement Learning Framework for Dialogue Management Optimisation},
journal = {IEEE Journal of Selected Topics in Signal Processing},
year = {2012},
volume = {6},
number = {8},
pages = {891-902},
month = {December},
note = {pdf},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp\'earnumber=6359744},
isbn = {1932-4553},
doi = {10.1109/JSTSP.2012.2229257},
abstract = {Reinforcement learning is now an acknowledged approach for optimising the interaction strategy of spoken dialogue systems. If the first considered algorithms were quite basic (like SARSA), recent works concentrated on more sophisticated methods. More attention has been paid to off-policy learning, dealing with the exploration-exploitation dilemma, sample efficiency or handling non-stationarity. New algorithms have been proposed to address these issues and have been applied to dialogue management. However, each algorithm often solves a single issue at a time, while dialogue systems exhibit all the problems at once. In this paper, we propose to apply the Kalman Temporal Differences (KTD) framework to the problem of dialogue strategy optimisation so as to address all these issues in a comprehensive manner with a single framework. Our claims are illustrated by experiments led on two real-world goal-oriented dialogue management frameworks, DIPPER and HIS. }
}