# Deep Reinforcement Learning for Multi-Domain Dialogue Systems

arXiv: https://arxiv.org/abs/1611.08675 · HTML: https://mlsanity.com/paper/1611.08675/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2016.

## Built on by (most cited first, 3 of 3)

- [A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning](https://mlsanity.com/paper/2202.13675/) (2022)
- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings](https://mlsanity.com/paper/2207.00468/) (2022)

## Abstract (arXiv)

Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems. We propose a method for multi-domain dialogue policy learning---termed NDQN, and apply it to an information-seeking spoken dialogue system in the domains of restaurants and hotels. Experimental results comparing DQN (baseline) versus NDQN (proposed) using simulations report that our proposed method exhibits better scalability and is promising for optimising the behaviour of multi-domain dialogue systems.