Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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
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
Gaussian processes for fast policy optimisation of POMDP-based dialogue managers
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Journal of Artificial Intelligence Research
Sample-efficient batch reinforcement learning for dialogue management optimization
ACM Transactions on Speech and Language Processing (TSLP)
A probabilistic framework for dialog simulation and optimal strategy learning
IEEE Transactions on Audio, Speech, and Language Processing
Statistical user simulation for spoken dialogue systems: what for, which data, which future?
SDCTD '12 NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data
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Designing dialog policies for voice-enabled interfaces is a tailoring job that is most often left to natural language processing experts. This job is generally redone for every new dialog task because cross-domain transfer is not possible. For this reason, machine learning methods for dialog policy optimization have been investigated during the last 15 years. Especially, reinforcement learning (RL) is now part of the state of the art in this domain. Standard RL methods require to test more or less random changes in the policy on users to assess them as improvements or degradations. This is called on policy learning. Nevertheless, it can result in system behaviors that are not acceptable by users. Learning algorithms should ideally infer an optimal strategy by observing interactions generated by a non-optimal but acceptable strategy, that is learning off-policy. In this contribution, a sample-efficient, online and off-policy reinforcement learning algorithm is proposed to learn an optimal policy from few hundreds of dialogues generated with a very simple handcrafted policy.