Papers with Deep-Q Networks
Feudal Reinforcement Learning for Dialogue Management in Large Domains (N18-2)
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Iñigo Casanueva, Paweł Budzianowski, Pei-Hao Su, Stefan Ultes, Lina M. Rojas-Barahona, Bo-Hsiang Tseng, Milica Gašić
| Challenge: | Reinforcement learning (RL) is a promising approach to model dialogue policy optimisation but fails to scale to large domains due to the curse of dimensionality. |
| Approach: | They propose a novel approach to dialogue policy optimisation using reinforcement learning . they propose to decompose the decision into two steps using a domain ontology . |
| Outcome: | The proposed architecture outperforms state-of-the-art in several dialogue domains without any additional reward signal. |