Linear least-squares algorithms for temporal difference learning
Machine Learning - Special issue on reinforcement learning
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Reinforcement Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Least-squares policy iteration
The Journal of Machine Learning Research
Information state and dialogue management in the TRINDI dialogue move engine toolkit
Natural Language Engineering
PARADISE: a framework for evaluating spoken dialogue agents
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
The Knowledge Engineering Review
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
Computational Linguistics
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations
The kernel recursive least-squares algorithm
IEEE Transactions on Signal Processing
A probabilistic framework for dialog simulation and optimal strategy learning
IEEE Transactions on Audio, Speech, and Language Processing
Kernel-Based Least Squares Policy Iteration for Reinforcement Learning
IEEE Transactions on Neural Networks
Sample-efficient batch reinforcement learning for dialogue management optimization
ACM Transactions on Speech and Language Processing (TSLP)
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Spoken dialogue management strategy optimization by means of Reinforcement Learning (RL) is now part of the state of the art. Yet, there is still a clear mismatch between the complexity implied by the required naturalness of dialogue systems and the inability of standard RL algorithms to scale up. Another issue is the sparsity of the data available for training in the dialogue domain which can not ensure convergence of most of RL algorithms. In this paper, we propose to combine a sample-efficient generalization framework for RL with a feature selection algorithm for the learning of an optimal spoken dialogue management strategy.