Variational denoising autoencoders and least-squares policy iteration for statistical dialogue managersVariational denoising autoencoders and least-squares policy iteration for statistical dialogue managers Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2021-09-272020enThe use of Reinforcement Learning (RL) approaches for dialogue policy optimization has been the new trend for dialogue management systems. Several methods have been proposed, which are trained on dialogue data to provide optimal system response. However, most of these approaches exhibit performance degradation in the presence of noise, poor scalability to other domains, as well as performance instabilities. To overcome these problems, we propose a novel approach based on the incremental, sample-efficient Least-Squares Policy Iteration (LSPI) algorithm, which is trained on compact, fixed-size dialogue state encodings, obtained from deep Variational Denoising Autoencoders (VDAE). The proposed scheme exhibits stable and noise-robust performance, which significantly outperforms the current state-of-the-art, even in mismatched noise environments.http://creativecommons.org/licenses/by/4.0/IEEE Signal Processing Letters27960–964 Diakoloukas Vasileios Διακολουκας Βασιλeioς Lygerakis Fotios Λυγερακης Φωτιος Lagoudakis Michail Λαγουδακης Μιχαηλ Kotti Margarita Institute of Electrical and Electronics Engineers Variational autoencoders Denoising Dialogue systems Sample-efficient statistical dialogue managers Least-squares policy iteration