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• W156932648 abstract "Learning dialogue strategies using the reinforcement learning framework is problematic due to its expensive computational cost. In this paper we propose an algorithm that reduces a state-action space to one which includes only valid state-actions. We performed experiments on full and reduced spaces using three systems (with 5, 9 and 20 slots) in the travel domain using a simulated environment. The task was to learn multi-goal dialogue strategies optimizing single and multiple confirmations. Average results using strategies learnt on reduced spaces reveal the following benefits against full spaces: 1) less computer memory (94% reduction), 2) faster learning (93% faster convergence) and better performance (8.4% less time steps and 7.7% higher reward). Index Terms: reinforcement learning, spoken dialogue systems." @default.
• W156932648 created "2016-06-24" @default.
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• W156932648 date "2006-09-17" @default.
• W156932648 modified "2023-10-01" @default.
• W156932648 title "Learning multi-goal dialogue strategies using reinforcement learning with reduced state-action spaces" @default.
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• W156932648 doi "https://doi.org/10.21437/interspeech.2006-149" @default.
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