# SimpleDS: A Simple Deep Reinforcement Learning Dialogue System

arXiv: https://arxiv.org/abs/1601.04574 · HTML: https://mlsanity.com/paper/1601.04574/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2016.

## Built on by (most cited first, 7 of 7)

- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.08570/) (2021)
- [Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System](https://mlsanity.com/paper/2205.04006/) (2022)
- [NLU for Game-based Learning in Real: Initial Evaluations](https://mlsanity.com/paper/2205.13754/) (2022)
- [End-to-End Evaluation of a Spoken Dialogue System for Learning Basic Mathematics](https://mlsanity.com/paper/2211.03511/) (2022)
- [A Simulated Experiment to Explore Robotic Dialogue Strategies for People with Dementia](https://mlsanity.com/paper/2104.08940/) (2021)
- [DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future](https://mlsanity.com/paper/2105.13806/) (2021)

## Abstract (arXiv)

This paper presents 'SimpleDS', a simple and publicly available dialogue system trained with deep reinforcement learning. In contrast to previous reinforcement learning dialogue systems, this system avoids manual feature engineering by performing action selection directly from raw text of the last system and (noisy) user responses. Our initial results, in the restaurant domain, show that it is indeed possible to induce reasonable dialogue behaviour with an approach that aims for high levels of automation in dialogue control for intelligent interactive agents.