# Enhancing End-to-End Multi-Task Dialogue Systems: A Study on Intrinsic Motivation Reinforcement Learning Algorithms for Improved Training and Adaptability

arXiv: https://arxiv.org/abs/2401.18040 · HTML: https://mlsanity.com/paper/2401.18040/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods.
- Builds on 30 of its 43 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Placed in

- Deep reinforcement learning methods / [RL training of language and agent models](https://mlsanity.com/deeprl/rl-training-of-language-and-agent-models/)

## Builds on (most cited first, 30 of 30)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Unifying Count-Based Exploration and Intrinsic Motivation](https://mlsanity.com/paper/1606.01868/) (2016)
- [Count-Based Exploration with Neural Density Models](https://mlsanity.com/paper/1703.01310/) (2017)
- [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://mlsanity.com/paper/1706.05125/) (2017)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems](https://mlsanity.com/paper/1508.01745/) (2015)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Episodic Curiosity through Reachability](https://mlsanity.com/paper/1810.02274/) (2018)
- [CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset](https://mlsanity.com/paper/2002.11893/) (2020)
- [Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1804.06512/) (2018)
- [ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems](https://mlsanity.com/paper/2002.04793/) (2020)
- [Neural User Simulation for Corpus-based Policy Optimisation for Spoken Dialogue Systems](https://mlsanity.com/paper/1805.06966/) (2018)
- [A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems](https://mlsanity.com/paper/1607.00070/) (2016)
- [Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog](https://mlsanity.com/paper/1908.10719/) (2019)
- [BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1711.05715/) (2017)
- [ConvLab: Multi-Domain End-to-End Dialog System Platform](https://mlsanity.com/paper/1904.08637/) (2019)
- [Personalizing a Dialogue System with Transfer Reinforcement Learning](https://mlsanity.com/paper/1610.02891/) (2016)
- [MLOps: A Review](https://mlsanity.com/paper/2308.10908/) (2023)
- [Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces](https://mlsanity.com/paper/1802.03753/) (2018)
- [From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban Search and Rescue](https://mlsanity.com/paper/2311.16958/) (2023)
- [Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows](https://mlsanity.com/paper/2306.02308/) (2023)
- [Feudal Reinforcement Learning for Dialogue Management in Large Domains](https://mlsanity.com/paper/1803.03232/) (2018)
- [Learning from Real Users: Rating Dialogue Success with Neural Networks for Reinforcement Learning in Spoken Dialogue Systems](https://mlsanity.com/paper/1508.03386/) (2015)
- [Show Us the Way: Learning to Manage Dialog from Demonstrations](https://mlsanity.com/paper/2004.08114/) (2020)
- [Harnessing customized built-in elements -- Empowering Component-Based Software Engineering and Design Systems with HTML5 Web Components](https://mlsanity.com/paper/2311.16601/) (2023)
- [Advancing Web Accessibility -- A guide to transitioning Design Systems from WCAG 2.0 to WCAG 2.1](https://mlsanity.com/paper/2312.02992/) (2023)

## Built on by (most cited first, 6 of 6)

- [Advancing Household Robotics: Deep Interactive Reinforcement Learning for Efficient Training and Enhanced Performance](https://mlsanity.com/paper/2405.18687/) (2024)
- [The Emotional Impact of Game Duration: A Framework for Understanding Player Emotions in Extended Gameplay Sessions](https://mlsanity.com/paper/2404.00526/) (2024)
- [A Novel Audio Representation for Music Genre Identification in MIR](https://mlsanity.com/paper/2404.01058/) (2024)
- [Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets](https://mlsanity.com/paper/2404.07225/) (2024)
- [Enhancing Music Genre Classification through Multi-Algorithm Analysis and User-Friendly Visualization](https://mlsanity.com/paper/2405.17413/) (2024)
- [Optimizing Photometric Light Curve Analysis: Evaluating Scipy's Minimize Function for Eclipse Mapping of Cataclysmic Variables](https://mlsanity.com/paper/2406.00071/) (2024)

## Abstract (arXiv)

End-to-end multi-task dialogue systems are usually designed with separate modules for the dialogue pipeline. Among these, the policy module is essential for deciding what to do in response to user input. This policy is trained by reinforcement learning algorithms by taking advantage of an environment in which an agent receives feedback in the form of a reward signal. The current dialogue systems, however, only provide meagre and simplistic rewards. Investigating intrinsic motivation reinforcement learning algorithms is the goal of this study. Through this, the agent can quickly accelerate training and improve its capacity to judge the quality of its actions by teaching it an internal incentive system. In particular, we adapt techniques for random network distillation and curiosity-driven reinforcement learning to measure the frequency of state visits and encourage exploration by using semantic similarity between utterances. Experimental results on MultiWOZ, a heterogeneous dataset, show that intrinsic motivation-based debate systems outperform policies that depend on extrinsic incentives. By adopting random network distillation, for example, which is trained using semantic similarity between user-system dialogues, an astounding average success rate of 73% is achieved. This is a significant improvement over the baseline Proximal Policy Optimization (PPO), which has an average success rate of 60%. In addition, performance indicators such as booking rates and completion rates show a 10% rise over the baseline. Furthermore, these intrinsic incentive models help improve the system's policy's resilience in an increasing amount of domains. This implies that they could be useful in scaling up to settings that cover a wider range of domains.