# A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management

arXiv: https://arxiv.org/abs/1711.11023 · HTML: https://mlsanity.com/paper/1711.11023/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2017.

## Built on by (most cited first, 8 of 8)

- [A Survey on Deep Reinforcement Learning for Audio-Based Applications](https://mlsanity.com/paper/2101.00240/) (2021)
- [NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue](https://mlsanity.com/paper/2204.13021/) (2022)
- [CrossAligner & Co: Zero-Shot Transfer Methods for Task-Oriented Cross-lingual Natural Language Understanding](https://mlsanity.com/paper/2203.09982/) (2022)
- [What Does The User Want? Information Gain for Hierarchical Dialogue Policy Optimisation](https://mlsanity.com/paper/2109.07129/) (2021)
- [Exploring ReAct Prompting for Task-Oriented Dialogue: Insights and Shortcomings](https://mlsanity.com/paper/2412.01262/) (2024)
- [Graph Neural Network Policies and Imitation Learning for Multi-Domain Task-Oriented Dialogues](https://mlsanity.com/paper/2210.05252/) (2022)
- [Is the User Enjoying the Conversation? A Case Study on the Impact on the Reward Function](https://mlsanity.com/paper/2101.05004/) (2021)
- [Findings from Experiments of On-line Joint Reinforcement Learning of Semantic Parser and Dialogue Manager with real Users](https://mlsanity.com/paper/2110.13213/) (2021)

## Abstract (arXiv)

Dialogue assistants are rapidly becoming an indispensable daily aid. To avoid the significant effort needed to hand-craft the required dialogue flow, the Dialogue Management (DM) module can be cast as a continuous Markov Decision Process (MDP) and trained through Reinforcement Learning (RL). Several RL models have been investigated over recent years. However, the lack of a common benchmarking framework makes it difficult to perform a fair comparison between different models and their capability to generalise to different environments. Therefore, this paper proposes a set of challenging simulated environments for dialogue model development and evaluation. To provide some baselines, we investigate a number of representative parametric algorithms, namely deep reinforcement learning algorithms - DQN, A2C and Natural Actor-Critic and compare them to a non-parametric model, GP-SARSA. Both the environments and policy models are implemented using the publicly available PyDial toolkit and released on-line, in order to establish a testbed framework for further experiments and to facilitate experimental reproducibility.